# Computer Science Department Courses

###### Note on Course Numbers

Each Carnegie Mellon course number begins with a two-digit prefix which designates the department offering the course (76-xxx courses are offered by the Department of English, etc.). Although each department maintains its own course numbering practices, typically the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. xx-6xx courses may be either undergraduate senior-level or graduate-level, depending on the department. xx-7xx courses and higher are graduate-level. Please consult the Schedule of Classes each semester for course offerings and for any necessary pre-requisites or co-requisites.

- 15-050 Study Abroad
- All Semesters

Students who are interested in studying abroad should first contact the Office of International Education. More information on Study Abroad is available on OIE's Study Abroad page and at the CS Undergraduate Office.

- 15-075 Computer Science Co-Op
- Fall and Spring

This course is meant for CS undergraduate students with a full-time internship that encompasses a summer and a contiguous semester, either Spring-Summer or Summer-Fall who wish to have this recorded on their academic transcript. Units posted for this course do not count toward any requirement for the CS undergraduate degree including free elective units. This course is not available to international students; consult with the Office for International Education for more information.

- 15-090 Computer Science Practicum
- Summer: 3 units

This course is for Computer Science students who wish to have an internship experience as part of their curriculum. Students are required to write a one-page summary statement prior to registration that explains how their internship connects with their CS curriculum, specifically on how it uses material they have learned as well as prepares them for future courses. Near the end of the internship, students will be required to submit a reflection paper that describes the work they did in more detail, including lessons learned about the work experience and how they utilized their CS education to work effectively. International students should consult with the Office of International Education for appropriate paperwork and additional requirements before registration. Units earned count toward the total required units necessary for degree completion; students should speak with an academic advisor for details. This course may be taken at most 3 times for a total of 9 units maximum. Students normally register for this course for use during the summer semester.

- 15-104 Introduction to Computing for Creative Practice
- Fall: 10 units

[IDeATe portal course] An introduction to fundamental computing principles and programming techniques for creative cultural practices, with special consideration to applications in music, design and the visual arts. Intended for students with little to no prior programming experience, the course develops skills and understanding of text-based programming in a procedural style, including idioms of sequencing, selection, iteration, and recursion. Topics include data organization (arrays, files, trees), interfaces and abstraction (modular software design, using sensor data and software libraries), basic algorithms (searching and sorting), and computational principles (randomness, concurrency, complexity). Intended for students following an IDEATE concentration or minor who have not taken 15-112.

- 15-110 Principles of Computing
- Fall: 10 units

A course in fundamental computing principles for students with minimal or no computing background. Programming constructs: sequencing, selection, iteration, and recursion. Data organization: arrays and lists. Use of abstraction in computing: data representation, computer organization, computer networks, functional decomposition, and application programming interfaces. Use of computational principles in problem-solving: divide and conquer, randomness, and concurrency. Classification of computational problems based on complexity, non-computable functions, and using heuristics to find reasonable solutions to complex problems. Social, ethical and legal issues associated with the development of new computational artifacts will also be discussed.

- 15-112 Fundamentals of Programming and Computer Science
- All Semesters: 12 units

A technical introduction to the fundamentals of programming with an emphasis on producing clear, robust, and reasonably efficient code using top-down design, informal analysis, and effective testing and debugging. Starting from first principles, we will cover a large subset of the Python programming language, including its standard libraries and programming paradigms. We will also target numerous deployment scenarios, including standalone programs, shell scripts, and web-based applications. This course assumes no prior programming experience. Even so, it is a fast-paced and rigorous preparation for 15-122. Students seeking a more gentle introduction to computer science should consider first taking 15-110. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course.

- 15-121 Introduction to Data Structures
- Fall and Spring: 10 units

A continuation of the process of program design and analysis for students with some prior programming experience (functions, loops, and arrays, not necessarily in Java). The course reinforces object-oriented programming techniques in Java and covers data aggregates, data structures (e.g., linked lists, stacks, queues, trees, and graphs), and an introduction to the analysis of algorithms that operate on those data structures.

Prerequisite: 15-112

- 15-122 Principles of Imperative Computation
- Fall and Spring: 10 units

For students with a basic understanding of programming (variables, expressions, loops, arrays, functions). Teaches imperative programming and methods for ensuring the correctness of programs. Students will learn the process and concepts needed to go from high-level descriptions of algorithms to correct imperative implementations, with specific application to basic data structures and algorithms. Much of the course will be conducted in a subset of C amenable to verification, with a transition to full C near the end. This course prepares students for 15-213 and 15-210. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course.

Prerequisite: 15-112

Corequisites: 15-151 and 21-127

Course Website: http://www.cs.cmu.edu/~rjsimmon/15122-s16/

- 15-128 Freshman Immigration Course
- Fall: 1 unit

The Freshman Immigration Course is taken by first-semester Computer Science majors on the Pittsburgh campus. The course is designed to acquaint incoming majors with computer science at CMU. Talks range from historical perspectives in the field to descriptions of the cutting edge research being conducted in the School of Computer Science. Enrollment is limited to SCS Freshmen ONLY.

Course Website: http://www.andrew.cmu.edu/course/15-128/

- 15-129 Freshman Immigration II
- Fall: 3 units

This course is ONLY offered at Carnegie Mellon in Qatar. Students and instructors will solve different problems each week by searching the Web and other likely places for answers. The problems will be submitted by other faculty who will grade the quality of the answers. Students will learn strategies and techniques for finding information on the Web more efficiently; learn when to start with a search engine, a subject-oriented directory, or other tools; explore and practice using advanced search syntax for major search engines; experience specialized search engines for images, sound, multimedia, newsgroups, and discussion lists as well as subject-specific search engines; discover valuable resources to help keep you up-to-date in this fast-changing environment.

- 15-131 Great Practical Ideas for Computer Scientists
- 2 units

THIS COURSE IS OPEN TO CS FRESHMAN ONLY. Throughout your education as a Computer Scientist at Carnegie Mellon, you will take courses on programming, theoretical ideas, logic, systems, etc. As you progress, you will be expected to pick up the so-called "tools of the trade." This course is intended to help you learn what you need to know in a friendly, low-stress, high-support way. We will discuss UNIX, LaTeX, debugging and many other essential tools. Laptop required. (Laptops will be available for those without their own laptops.)

- 15-150 Principles of Functional Programming
- Fall and Spring: 10 units

An introduction to programming based on a "functional" model of computation. The functional model is a natural generalization of algebra in which programs are formulas that describe the output of a computation in terms of its inputs—-that is, as a function. But instead of being confined to real- or complex-valued functions, the functional model extends the algebraic view to a very rich class of data types, including not only aggregates built up from other types, but also functions themselves as values. This course is an introduction to programming that is focused on the central concepts of function and type. One major theme is the interplay between inductive types, which are built up incrementally; recursive functions, which compute over inductive types by decomposition; and proof by structural induction, which is used to prove the correctness and time complexity of a recursive function. Another major theme is the role of types in structuring large programs into separate modules, and the integration of imperative programming through the introduction of data types whose values may be altered during computation. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course.

Prerequisites: (15-151 or 21-127) and 15-112

- 15-151 Mathematical Foundations for Computer Science
- Fall: 10 units

This course is offered to incoming Computer Science freshmen and focuses on the fundamental concepts in Mathematics that are of particular interest to Computer Science such as logic, sets,induction, functions, and combinatorics. These topics are used as a context in which students learn to formalize arguments using the methods of mathematical proof. This course uses experimentation and collaboration as ways to gain better understanding of the material. Open to CS freshmen only.

Corequisite: 15-152

- 15-199 Special Topics: Discovering Logic
- Intermittent: 3 units

This course is ONLY offered at Carnegie Mellon in Qatar. This course has the purpose of introducing first-year Computer Science students to elements of formal logic as well as to the historical context in which this discipline developed. As all subsequent courses in the CS curriculum rely on students having mastered basic logical notions and skills, it will test and enhance your preparation, thereby putting you in a better position to succeed in the program. It will also help you understand and appreciate how CS came about since Computer Science grew out of logic. The specific knowledge and skills you will learn in is course include: an enhanced ability to research topics, give presentations and write technical prose, some elementary logic, some historical depth into Computer Science, mathematics and logic itself. This course is open to Computer Science freshmen only.

- 15-210 Parallel and Sequential Data Structures and Algorithms
- Fall and Spring: 12 units

Teaches students about how to design, analyze, and program algorithms and data structures. The course emphasizes parallel algorithms and analysis, and how sequential algorithms can be considered a special case. The course goes into more theoretical content on algorithm analysis than 15-122 and 15-150 while still including a significant programming component and covering a variety of practical applications such as problems in data analysis, graphics, text processing, and the computational sciences. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course.

Prerequisites: 15-150 and 15-122

- 15-213 Introduction to Computer Systems
- Fall and Spring: 12 units

This course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systems-level issues is required. Topics covered include: machine-level code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation. NOTE FOR GRADUATE STUDENTS: This course is not open to graduate students beginning Spring 2015. Graduate students must register for 15-513 instead.

Prerequisite: 15-122

- 15-214 Principles of Software Construction: Objects, Design, and Concurrency
- Fall and Spring: 12 units

Software engineers today are less likely to design data structures and algorithms from scratch and more likely to build systems from library and framework components. In this course, students engage with concepts related to the construction of software systems at scale, building on their understanding of the basic building blocks of data structures, algorithms, program structures, and computer structures. The course covers technical topics in four areas: (1) concepts of design for complex systems, (2) object oriented programming, (3) static and dynamic analysis for programs, and (4) concurrent and distributed software. Student assignments involve engagement with complex software such as distributed massively multi-player game systems and frameworks for graphical user interaction.

Prerequisites: (15-121 or 15-122) and (15-151 or 21-127)

- 15-221 Technical Communication for Computer Scientists
- All Semesters: 9 units

The course is designed for sophomore computer science majors to improve their abilities in practical, professional communications (both written and oral). It aims to help students compose clear, concise technical writings and oral presentations for multi-level audiences. Assignments include technical definitions, descriptions, instructions, process explanations, abstracts, memos, and research reports. Assignments may incorporate recent computer science research at Carnegie Mellon, projects in related technical courses, and professional case studies. Sophomores will likely find the course more useful if they have either had an internship or faculty-supervised research, including SURG projects prior to enrollment.

Prerequisite: 76-101

- 15-237 Special Topic: Cross-Platform Mobile Web Apps
- 12 units

An introduction to writing cross-platform mobile web apps. Using a tool chain based on HTML5, CSS3, JavaScript, and a variety of supporting frameworks, we will write apps that are effectively designed both for desktop and mobile browsers, and which can be converted into native apps for Android, iOS, and Windows Phone 7 devices. Additional topics will include designing user interfaces for mobile devices, accessing mobile device API's (such as accelerometer, GPS, compass, or camera), and power management issues. While this course focuses on browser-side technologies, we will briefly explore JavaScript-based server-side technologies (though students should consider 15-437 for extensive treatment of server-side topics). Note that we will not be writing native apps in Objective-C for iOS nor in Java for Android, though we may include some brief exposure to these technologies near the end of the course.

Prerequisite: 15-112

- 15-251 Great Theoretical Ideas in Computer Science
- Fall and Spring: 12 units

This course is about how to use theoretical ideas to formulate and solve problems in computer science. It integrates mathematical material with general problem solving techniques and computer science applications. Examples are drawn from algorithms, complexity theory, game theory, probability theory, graph theory, automata theory, algebra, cryptography, and combinatorics. Assignments involve both mathematical proofs and programming. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course.

Prerequisites: (15-150 or 15-122) and (15-151 or 21-127)

Course Website: http://www.cs.cmu.edu/~15251

- 15-294 Special Topic: Rapid Prototyping Technologies
- Intermittent: 5 units

This mini-course introduces students to rapid prototyping technologies with a focus on laser cutting and 3D printing. The course has three components: 1) A survey of rapid prototyping and additive manufacturing technologies, the maker and open source movements, and societal impacts of these technologies; 2) An introduction to the computer science behind these technologies: CAD tools, file formats, slicing algorithms; 3) Hands-on experience with SolidWorks, laser cutting, and 3D printing, culminating in student projects (e.g. artistic creations, functional objects, replicas of famous calculating machines, etc.). Please note that there will be a usage/materials fee for this course.

Prerequisites: 15-112 or 15-104

- 15-295 Competition Programming and Problem Solving
- Fall and Spring: 5 units

Each year, Carnegie Mellon fields two teams for participation in the ACM-ICPC Regional Programming Contest. During many recent years, one of those teams has earned the right to represent Carnegie Mellon at the ACM-ICPC World Finals. This course is a vehicle for those who consistently and rigorously train in preparation for the contests to earn course credit for their effort and achievement. Preparation involves the study of algorithms, the practice of programming and debugging, the development of test sets, and the growth of team, communication, and problem solving skills. Neither the course grade nor the number of units earned are dependent on ranking in any contest. Students are not required to earn course credit to participate in practices or to compete in ACM-ICPC events.

Prerequisite: 15-122

- 15-300 Research and Innovation in Computer Science
- Fall: 9 units

This Fall course is the first part of a two-course sequence that is designed to help prepare students to invent the future state-of-the-art in the field of computer science. Course topics will include the following: an overview of important things to know about how research and innovation works in the field of computer science; a survey of the current cutting- edge of computer science research, both here at Carnegie Mellon and elsewhere; critical thinking skills when reading research publications that disagree with each other; strategies for coping with open-ended problems; and technical communication skills for computer scientists. Students will also match up with a faculty mentor for a potential Technology Innovation Project (to be performed in the Spring), put together a detailed plan of attack for that project, and start to get up to speed (including background reading, etc.). This course can be used to satisfy the Technical Communications requirement for the CS major.

Prerequisites: 15-213 and 15-210 and 15-251 and 76-101

- 15-312 Foundations of Programming Languages
- Spring: 12 units

This course discusses in depth many of the concepts underlying the design, definition, implementation, and use of modern programming languages. Formal approaches to defining the syntax and semantics are used to describe the fundamental concepts underlying programming languages. A variety of programming paradigms are covered such as imperative, functional, logic, and concurrent programming. In addition to the formal studies, experience with programming in the languages is used to illustrate how different design goals can lead to radically different languages and models of computation.

Prerequisites: (15-210 and 15-251) or 15-212

- 15-313 Foundations of Software Engineering
- Fall: 12 units

Students gain exposure to the fundamentals of modern software engineering. This includes both core CS technical knowledge and the means by which this knowledge can be applied in the practical engineering of complex software. Topics related to software artifacts include design models, patterns, coding, static and dynamic analysis, testing and inspection, measurement, and software architecture and frameworks. Topics related to software process include modeling, requirements engineering, process models and evaluation, team development, and supply chain issues including outsourcing and open source. This course has a strong technical focus, and will include both written and programming assignments. Students will get experience with modern software engineering tools.

Prerequisite: 15-214

- 15-314 Special Topic: Programming Language Semantics
- Spring: 12 units

This course is designed for advanced undergraduates with interests in the mathematical and logical foundations of programming languages. The course introduces the foundational concepts and fundamental techniques of the most prominent and successful approaches to programming language semantics that have been developed. Broadly speaking, semantics is concerned with the provision of mathematical meanings to programs, at an appropriate level of abstraction, to allow formalization of program behavior and facilitate proofs of correctness. Our aim is to demonstrate the utility of a scientific approach to programming and languages. We focus on the most important and most general frameworks for semantic description: denotational and operational semantics. These frameworks are widely applicable and offer complementary approaches to language definition, with various advantages. We also discuss formal specifications, and logics of program correctness. We make extensive use of mathematical and structural induction, and computational induction. We use semantics to describe program behavior, guide the development of correct programs, specify and prove the correctness of a compiler, validate program logics, and derive laws of program equivalence. We discuss imperative and functional languages, sequential and parallel, high-level and low-level, as time permits.

Prerequisites: 15-251 and 15-150

- 15-317 Constructive Logic
- Fall: 9 units

This multidisciplinary junior-level course is designed to provide a thorough introduction to modern constructive logic, its roots in philosophy, its numerous applications in computer science, and its mathematical properties. Some of the topics to be covered are intuitionistic logic, inductive definitions, functional programming, type theory, realizability, connections between classical and constructive logic, decidable classes.

Prerequisite: 15-150

Course Website: http://symbolaris.com/course/constlog16.html

- 15-319 Cloud Computing
- Fall and Spring: 9 units

This course gives students an overview of Cloud Computing, which is the delivery of computing as a service over a network, whereby distributed resources are rented, rather than owned, by an end user as a utility. Students will study its enabling technologies, building blocks, and gain hands-on experience through projects utilizing public cloud infrastructures. Cloud computing services are widely adopted by many organizations across domains. The course will introduce the cloud and cover the topics of data centers, software stack, virtualization, software defined networks and storage, cloud storage, and programming models. We will start by discussing the clouds motivating factors, benefits, challenges, service models, SLAs and security. We will describe several concepts behind data center design and management, which enable the economic and technological benefits of the cloud paradigm. Next, we will study how CPU, memory and I/O resources, network (SDN) and storage (SDS) are virtualized, and the key role of virtualization to enable the cloud. Subsequently, students will study cloud storage concepts like data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage using HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift as case studies. Finally, students will study the MapReduce, Spark and GraphLab programming models. Students will work with Amazon Web Services and Microsoft Azure, to rent and provision compute resources and then program and deploy applications using these resources. Students will develop and evaluate scaling and load balancing solutions, work with cloud storage systems, and develop applications in several programming paradigms. 15619 students must complete an extra team project which entails designing and implementing a cost- and performance-sensitive web-service for querying big data.

Prerequisite: 15-213

- 15-321 Research Methods for Experimental Computer Science
- Fall: 9 units

The success or failure of an experiment can turn on the details of how the experiment was performed — the experimental method. It is critical that the methodology be consistent with whatever research hypothesis is being pursued. The goal of this project-based course is to give students familiarity with and appreciation for the subtleties of a range of experimental techniques essential to high-quality empirical research. The course is based on a project that will illustrate important concepts of research methods. Example topics include user-study design and operation, data gathering, data diagnosis, experiment design and execution, signal detection, performance evaluation, error analysis, reporting results, etc. The project will explore the challenging open problem of keystroke dynamics, a biometric regime which seeks to identify/authenticate/discriminate users on the basis of their typing styles. In this context, numerous methodological issues provide the stimulus for learning how to perform empirical research from the beginning to the end of a project. Lectures will present necessary background material about the problem area and experimental methods; homework assignments and a team project will give students guided, hands-on, research and practical experience. At the end of the course, students will be acquainted with the basic issues in experimental methods, and will be able to perform independent work using the lessons learned and resources provided. Enrollment is limited to graduate students, juniors, and seniors. It will be helpful for students to have some experience with scripting languages (e.g., Perl, Python, Tcl, etc), statistically-oriented packages (e.g., R, Matlab, Weka) or other implementations of various machine-learning-type classifiers. This course can be used to satisfy the Lab requirement for the Computer Science major.

Prerequisite: 15-213

- 15-322 Introduction to Computer Music
- Spring: 9 units

Computers are used to synthesize sound, process signals, and compose music. Personal computers have replaced studios full of sound recording and processing equipment, completing a revolution that began with recording and electronics. In this course, students will learn the fundamentals of digital audio, basic sound synthesis algorithms, and techniques for digital audio effects and processing. Students will apply their knowledge in programming assignments using a very high-level programming language for sound synthesis and composition. In a final project, students will demonstrate their mastery of tools and techniques through music composition or by the implementation of a significant sound-processing technique.

Prerequisite: 15-112

- 15-323 Computer Music Systems and Information Processing
- Spring: 9 units

This course presents concepts and techniques for representing and manipulating discrete music information, both in real time and off line. Representations of music as explicitly timed event sequences will be introduced, and students will learn how to build efficient run-time systems for event scheduling, tempo control, and interactive processing. The MIDI protocol is used to capture real-time performance information and to generate sound. The course will also cover non-real-time processing of music data, including Markov models, style recognition, computer accompaniment, query-by-humming, and algorithmic composition. This course is independent of, and complementary to 15-322, Introduction to Computer Music, which focuses on sound synthesis and signal processing.

Prerequisite: 15-122

- 15-346 Special Topic: Perspectives in Computer Architecture
- Intermittent: 6 units

This course is ONLY offered at Carnegie Mellon in Qatar. This course will provide various perspectives in the field of computer architecture by world renowned scientists. The course will bring together basic architecture principles and designs of uniprocessor and multicore computers. First, we introduce the Von-Neuman architecture and the major hardware components of a modern computer. Students will learn how to describe an architecture design using a Hardware Description Language such as Verilog. Second, we will learn basic architectural techniques including instruction level parallelism, pipelining and cache memory. An overview of multicore architectures, specifically on how they differ from uniprocessor ones, the promises they offer, and the serious challenges they bring, will be provided. We will also discuss cache organization techniques in multicores. Students will gain insight into the designing principles that dominated past processor architectures and how they will continue to change for future processor design targeting emerging technologies such as mobile and wearable computing. The concepts delivered in the lectures will be reinforced and extended through student presentations on multiple directions in computer architecture.

Prerequisite: 15-213

- 15-348 Embedded Systems
- Fall: 9 units

This course is offered only at Carnegie Mellon's campus in Qatar. This course covers the broad range of foundational skills that apply across all embedded computer system application areas, from thermostats to self-driving vehicles. The emphasis is at the layer where hardware meets software. Topics include microcontroller hardware, assembly language, embedded C programming, analog I/O, timers, code optimization, interrupts, and concurrency. Real world engineering practices, constraints, and example applications are integrated throughout the course. Weekly hands-on hardware and software experiences with an industry-strength automotive embedded controller are coordinated with the lecture content to reinforce core skills.

Prerequisite: 15-122

- 15-349 Introduction to Computer and Network Security
- Fall: 9 units

This course is ONLY offered at Carnegie Mellon in Qatar. This course is meant to offer Computer Science undergraduate students in their junior or senior year a broad overview of the field of computer security. Students will learn the basic concepts in computer security including software vulnerability analysis and defense, networking and wireless security, applied cryptography, as well as ethical, legal, social and economic facets of security. Students will also learn the fundamental methodology for how to design and analyze security critical systems.

Prerequisite: 15-122

- 15-351 Algorithms and Advanced Data Structures
- Intermittent: 12 units

The objective of this course is to study algorithms for general computational problems, with a focus on the principles used to design those algorithms. Efficient data structures will be discussed to support these algorithmic concepts. Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness. Although this course may have a few programming assignments, it is primarily not a programming course. Instead, it will focus on the design and analysis of algorithms for general classes of problems. This course is not open to CS graduate students who should consider taking 15-651 instead. THIS COURSE IS NOT OPEN TO COMPUTER SCIENCE MAJORS OR MINORS.

Prerequisites: 15-121 or 15-122

- 15-354 Computational Discrete Mathematics
- Fall: 12 units

This course is about the computational aspects of some of the standard concepts of discrete mathematics (relations, functions, logic, graphs, algebra, automata), with emphasis on efficient algorithms. We begin with a brief introduction to computability and computational complexity. Other topics include: iteration, orbits and fixed points, order and equivalence relations, propositional logic and satisfiability testing, finite fields and shift register sequences, finite state machines, and cellular automata. Computational support for some of the material is available in the form of a Mathematica package.

Prerequisites: 21-228 or 15-251

Course Website: http://www.cs.cmu.edu/~cdm/

- 15-355 Modern Computer Algebra
- Fall: 9 units

The goal of this course is to investigate the relationship between algebra and computation. The course is designed to expose students to algorithms used for symbolic computation, as well as to the concepts from modern algebra which are applied to the development of these algorithms. This course provides a hands-on introduction to many of the most important ideas used in symbolic mathematical computation, which involves solving system of polynomial equations (via Groebner bases), analytic integration, and solving linear difference equations. Throughout the course the computer algebra system Mathematica will be used for computation.

Prerequisites: 15-251 or 21-228

Course Website: http://www.andrew.cmu.edu/course/15-355/

- 15-359 Probability and Computing
- Spring: 12 units

Probability theory has become indispensable in computer science. In areas such as artificial intelligence and computer science theory, probabilistic methods and ideas based on randomization are central. In other areas such as networks and systems, probability is becoming an increasingly useful framework for handling uncertainty and modeling the patterns of data that occur in complex systems. This course gives an introduction to probability as it is used in computer science theory and practice, drawing on applications and current research developments as motivation and context. Topics include combinatorial probability and random graphs, heavy tail distributions, concentration inequalities, various randomized algorithms, sampling random variables and computer simulation, and Markov chains and their many applications, from Web search engines to models of network protocols. The course will assume familiarity with 3-D calculus and linear algebra.

Prerequisites: 21-259 and 21-241 and 15-251

Course Website: http://www.cs.cmu.edu/~harchol/15359/class.html

- 15-365 Experimental Animation
- Spring: 12 units

[IDeATe course] This class will explore animation from the student's perspective with a sense of investigation toward both form and content. Topics in the class will include non-linear narrative, visual music, puppet and non-traditional materials, manipulation of motion and performance capture data, immersive environments.

Prerequisite: 15-213

- 15-369 Special Topics: Perceptual Computing
- Intermittent: 9 units

This course is ONLY offered at Carnegie Mellon in Qatar. What can today's computers see, hear, and feel? This project-based course is designed to provide students exposure to the state-of-the-art in machine perception and the algorithms behind them. Student groups will design a perceptual computing project around Intel's Creative Camera or Microsoft's Kinect. Students will learn to use tools in face detection and recognition, hand and finger tracking, and speech recognition, along with algorithms to make decisions based on these input modalities.

Prerequisites: 21-241 and 15-122

- 15-381 Artificial Intelligence: Representation and Problem Solving
- Fall: 9 units

This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e. satisficing or optimal) decisions towards the achievement of goals. The search and problem solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world, how to learn from experience, and how to learn decision rules from data. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents learn. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents. Other lectures will introduce additional aspects of AI, including natural language processing, web-based search engines, industrial applications, autonomous robotics, and economic/game-theoretic decision making.

Prerequisite: 15-122

- 15-383 Introduction to Text Processing
- Fall: 6 units

Text processing is a mini-course about text basic techniques of processing human language in text format. The course has theoretical and hands-on components. In the theoretical component, the course will discuss challenges in processing human languages, and review the basics of statistics and probability theory and their application to language problems. In the hands-on part, students will learn about Python programming and use it to process large volumes of text using various techniques. The processing will range from simple steps such as tokenization and part-of-speech tagging to full-fledged applications such as statistical machine translation, search and document/topic classification. The course is suited for junior and senior students in CS and IS.

Prerequisites: 15-121 or 15-122

- 15-385 Introduction to Computer Vision
- Spring: 6 units

An introduction to the science and engineering of computer vision, i.e. the analysis of the patterns in visual images with the view to understanding the objects and processes in the world that generate them. Major topics include image formation and sensing, fourier analysis, edge and contour detection, inference of depth, shape and motion, classification, recognition, tracking, and active vision. The emphasis is on the learning of fundamental mathematical concepts and techniques and applying them to solve real vision problems. The discussion will also include comparison with human and animal vision from psychological and biological perspectives. Students will learn to think mathematically and develop skills in translating ideas and mathematical thoughts into programs to solve real vision problems.

Prerequisites: 15-122 and 21-241

- 15-386 Neural Computation
- Spring: 9 units

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities — such as perception, language, motor control, and learning — by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

Prerequisites: 15-112 and 21-122

- 15-387 Computational Perception
- Fall: 9 units

In this course, we will first cover the biological and psychological foundational knowledge of biological perceptual systems, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. The course will focus on vision this year, but will also touch upon other sensory modalities. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. Topics include perceptual representation and inference, perceptual organization, perceptual constancy, object recognition, learning and scene analysis. Prerequisites: First year college calculus, some basic knowledge of linear algebra and probability and some programming experience are desirable.

Prerequisites: 15-112 and 21-241 and 21-122

- 15-388 Practical Data Science
- Fall: 9 units

Data science is the study and practice of how we can extract insight and knowledge from large amounts of data. This course provides a practical introduction to the "full stack" of data science analysis, including data collection and processing, data visualization and presentation, statistical model building using machine learning, and big data techniques for scaling these methods. Topics covered include: collecting and processing data using relational methods, time series approaches, graph and network models, free text analysis, and spatial geographic methods; analyzing the data using a variety of statistical and machine learning methods include linear and non-linear regression and classification, unsupervised learning and anomaly detection, plus advanced machine learning methods like kernel approaches, boosting, or deep learning; visualizing and presenting data, particularly focusing the case of high-dimensional data; and applying these methods to big data settings, where multiple machines and distributed computation are needed to fully leverage the data.Students will complete weekly programming homework that emphasize practical understanding of the methods described in the course. In addition, students will develop a tutorial on an advanced topic, and will complete a group project that applies these data science techniques to a practical application chosen by the team; these two longer assignments will be done in lieu of a midterm or final.

Prerequisite: 15-112

Course Website: http://www.datasciencecourse.org

- 15-390 Entrepreneurship for Computer Science
- Fall: 9 units

This course is designed to develop skills related to entrepreneurship and innovation for non-business undergraduate and graduate students in the School of Computer Science. The course assumes no background courses in business and is appropriate for those who are interested in bringing innovations to market either through new companies or existing companies. The course provides an overview of entrepreneurship and innovation, develops an entrepreneurial frame of mind, and provides a framework for learning the rudiments of how to generate ideas. Students come up with or are presented with potential ideas and learn how to develop these ideas into opportunities, and to explore their potential for becoming viable businesses. They learn how to do market research, to develop go-to-market strategies, value propositions and to differentiate their products or services from potential competitors. The focus is on understanding and developing strategies for approaching the key elements of the entrepreneurial process...opportunity, resources and team. The course consists of a balance of lectures, case studies and encounters with entrepreneurs, investors and business professionals. The students are exposed to financial and intellectual property issues, and encounter a real world perspective on entrepreneurship, innovation and leadership. The output of the course is a mini-business plan or venture opportunity screening document that can be developed into a business plan in a subsequent course entitled New Venture Creation or through independent study.

Prerequisites: 76-270 or 15-221 or 15-300

- 15-392 Special Topic: Secure Programming
- Spring: 9 units

This course provides a detailed explanation of common programming errors in C and C++ and describes how these errors can lead to software systems that are vulnerable to exploitation. The course concentrates on security issues intrinsic to the C and C++ programming languages and associated libraries. It does not emphasize security issues involving interactions with external systems such as databases and web servers, as these are rich topics on their own. Topics to be covered include the secure and insecure use of integers, arrays, strings, dynamic memory, formatted input/output functions, and file I/O.

Prerequisite: 15-213

Course Website: https://www.securecoding.cert.org/confluence/display/sci/15392+Secure+Programming

- 15-400 Research Practicum in Computer Science
- Spring: 12 units

This Spring course is the second part of a two-course sequence that is designed to help prepare students to invent the future state-of-the-art in the field of computer science. Building directly upon 15-300 (the prerequisite for this course), students will conduct a semester-long independent research project, under the guidance of both the course staff and a faculty project mentor. The course does not meet for lecture or recitations. Instead, the students will spend their time working on their research projects, and will also meet with course staff on a bi-weekly basis to discuss their progress. Students will prepare a written report and a poster presentation at the end of the semester to describe what they have accomplished.

Prerequisite: 15-300

- 15-405 Engineering Distributed Systems
- Spring: 9 units

This is a course for students with strong design and implementation skills who are likely to pursue careers as software architects and lead engineers. It may be taken by well-prepared undergraduates with excellent design and implementation skills in low-level systems programing. The course assumes a high level of proficiency in all aspects of operating system design and implementation. This course will help students prepare for leadership roles in creating and evolving the complex, large-scale computer systems that society will increasingly depend on in the future. The course will teach the organizing principles of such systems, identifying a core set of versatile techniques that are applicable across many system layers. Students will acquire the knowledge base, intellectual tools, hands-on skills and modes of thought needed to build well-engineered computer systems that withstand the test of time, growth in scale, and stresses of live use. Topics covered include: caching, prefetching, damage containment, scale reduction, hints, replication, hash-based techniques, and fragmentation reduction. A substantial project component is an integral part of the course. A high level of proficiency in systems programming is expected. If you do not have the 15-410 prerequisite you will need to get approval from the faculty.

Prerequisite: 15-410

- 15-410 Operating System Design and Implementation
- Fall and Spring: 15 units

Operating System Design and Implementation is a rigorous hands-on introduction to the principles and practice of operating systems. The core experience is writing a small Unix-inspired OS kernel, in C with some x86 assembly language, which runs on a PC hardware simulator (and on actual PC hardware if you wish). Work is done in two-person teams, and "team programming" skills (source control, modularity, documentation) are emphasized. The size and scope of the programming assignments typically result in students significantly developing their design, implementation, and debugging abilities. Core concepts include the process model, virtual memory, threads, synchronization, and deadlock; the course also surveys higher-level OS topics including file systems, interprocess communication, networking, and security. Students, especially graduate students, who have not satisfied the prerequisite at Carnegie Mellon are strongly cautioned - to enter the class you must be able to write a storage allocator in C, use a debugger, understand 2's-complement arithmetic, and translate between C and x86 assembly language. The instructor may require you to complete a skills assessment exercise before the first week of the semester in order to remain registered in the class. Auditing: this course is usually full, and we generally receive many more requests to audit than we can accept. If you wish to audit, please have your advisor contact us before the semester begins to discuss your educational goals.

Prerequisite: 15-213

- 15-411 Compiler Design
- Fall: 15 units

This course covers the design and implementation of compiler and run-time systems for high-level languages, and examines the interaction between language design, compiler design, and run-time organization. Topics covered include syntactic and lexical analysis, handling of user-defined types and type-checking, context analysis, code generation and optimization, and memory management and run-time organization.

Prerequisite: 15-213

Course Website: http://symbolaris.com/course/compiler12.html

- 15-412 Operating System Practicum
- Fall

The goal of this class is for students to acquire hands-on experience with operating-system code as it is developed and deployed in the real world. Groups of two to four students will select, build, install, and become familiar with an open-source operating system project; propose a significant extension or upgrade to that project; and develop a production-quality implementation meeting the coding standards of that project. Unless infeasible, the results will be submitted to the project for inclusion in the code base. Variations on this theme are possible at the discretion of the instructor. For example, it may be possible to work within the context of a non-operating-system software infrastructure project (window system, web server, or embedded network device kernel) or to extend a 15-410 student kernel. In some situations students may work alone. Group membership and unit count (9 units versus 12) will be decided by the third week of the semester. Contributing to a real-world project will involve engaging in some mixture of messy, potentially open-ended activities such as: learning a revision control system, writing a short design document, creating and updating a simple project plan, participating in an informal code review, synthesizing scattered information about hardware and software, classifying and/or reading large amounts of code written by various people over a long period of time, etc.

Prerequisite: 15-410

- 15-413 Software Engineering Practicum
- Spring: 12 units

15-413, the Software Engineering Practicum, gives you an opportunity to learn and apply software engineering skills to real-world software projects. This semester 15-413 will be part of the Facebook Open Academy program, providing students an opportunity to work with each other and students from other universities to make substantial contributions to external open source projects. Enrollment in this course is extremely limited and is possible only with explicit permission from the course instructors. See the course web site for details. After completing this course, students will: * Have substantial experience contributing to real, open source software projects. * Know and be able to use the software development processes used by open source software projects, with a deep understanding of the processes and techniques you will encounter in a software engineering workplace. * Be able to evaluate and discuss aspects of software management including project planning, requirements analysis, architecture, detailed design, quality assurance, and risk management. * Have substantial first-hand understanding of the software engineering realities that drive software engineering practices, with concrete experience and active reflection on your course experiences.

Course Website: http://www.cs.cmu.edu/~charlie/courses/15-413/

- 15-414 Bug Catching: Automated Program Verification and Testing
- Intermittent: 9 units

Many CS and ECE students will be developing software and hardware that must be ultra reliable at some point in their careers. Logical errors in such designs can be costly, even life threatening. There have already been a number of well publicized errors like the Intel Pentium floating point error and the Arian 5 crash. In this course we will study tools for finding and preventing logical errors. Three types of tools will be studied: automated theorem proving, state exploration techniques like model checking and tools based on static program analysis. Although students will learn the theoretical basis for such tools, the emphasis will be on actually using them on real examples. This course can be used to satisfy the Logic & Languages requirement for the Computer Science major.

Prerequisites: 15-122 and 15-251

- 15-415 Database Applications
- Fall: 12 units

This course covers the fundamental topics for Database Management Systems: Database System Architectural Principles (ACID properties; data abstraction; external, conceptual, and internal schemata; data independence; data definition and data manipulation languages), Data models (entity-relationship and relational data models; data structures, integrity constraints, and operations for each data model; relational query languages: SQL, algebra, calculus), Theory of database design (functional dependencies; normal forms; dependency preservation; information loss), Query Optimization (equivalence of expressions, algebraic manipulation; optimization of selections and joins), Storage Strategies (indices, B-trees, hashing), Query Processing (execution of sort, join, and aggregation operators), and Transaction Processing (recovery and concurrency control).

Prerequisites: 15-210 and 15-213

Course Website: http://15415.courses.cs.cmu.edu/

- 15-417 HOT Compilation
- Intermittent: 12 units

The course covers the implementation of compilers for higher-order, typed languages such as ML and Haskell, and gives an introduction to type-preserving compilation. Topics covered include type inference, elaboration, CPS conversion, closure conversion, garbage collection, phase splitting, and typed assembly language.

Prerequisites: 15-312 or 15-317

- 15-418 Parallel Computer Architecture and Programming
- Spring: 12 units

The fundamental principles and engineering tradeoffs involved in designing modern parallel computers, as well as the programming techniques to effectively utilize these machines. Topics include naming shared data, synchronizing threads, and the latency and bandwidth associated with communication. Case studies on shared-memory, message-passing, data-parallel and dataflow machines will be used to illustrate these techniques and tradeoffs. Programming assignments will be performed on one or more commercial multiprocessors, and there will be a significant course project.

Prerequisite: 15-213

- 15-421 Information Security and Privacy
- Fall: 12 units

The objective of this course is to introduce students to the technologies of Web Commerce, Security and Privacy as well as to related business, policy and usability issues. Content: Over the past 15 years, the Web has become an integral part of our daily life, whether at home or at work. This course provides students with an overview of the technologies and practices associated with Web Security, Privacy and Commerce. In the process, students will learn what it takes to design and develop successful web applications and services, reconciling security, privacy, usability and business considerations. The course is organized around two parts: Part I - Web Security & Privacy Technologies The big picture, gentle introduction to cryptography, digital signatures, key management, authentication, Internet security protocols, certificates & PKI, decentralized trust management, privacy enhancing technologies, electronic payments. Part II - Web Commerce The big picture, Internet marketing & personalization, search engines, B2B and electronic markets, P2P, Web 2.0, Mobile Commerce, social networking. Format: Lectures (including guest lectures), discussions, student presentations, and class projects. Prerequisites: 15122 or 15211

Prerequisites: 15-112 and 76-101

Course Website: http://www.cs.cmu.edu/~sadeh/15%20421.html

- 15-423 Special Topic: Digital Signal Processing for Computer Science
- Spring: 12 units

Digital signals comprise a large fraction of the data analyzed by computer scientists. Sound, e.g. speech and music, images, radar and many other signal types that were conventionally considered to be the domain of the Electrical engineer are now also in the domain of computer scientists, who must analyze them, make inferences, and develop machine learning techinques to analyze, classify and reconstruct such data. In this course we will cover the basics of Digital Signal Processing. We will concentrate on the basic mathematical formulations, rather than in-depth implementation details. We will cover the breadth of topics, beginning with the basics of signals and their representations, the theory of sampling, important transform representations, key processing techniques, and spectral estimation.

Prerequisites: (15-122 or 15-112) and (21-325 or 36-217 or 36-225 or 36-625 or 15-359)

- 15-424 Foundations of Cyber-Physical Systems
- Intermittent: 12 units

Cyber-physical systems (CPSs) combine cyber effects (computation and/or communication) with physical effects (motion or other physical processes). Designing algorithms to control CPSs, such as those in cars, aircraft and robots, is challenging due to their tight coupling with physical behavior. At the same time, it is vital that these algorithms be correct, since we rely on CPSs for safety-critical tasks like keeping aircraft from colliding. Students in this course will understand the core principles behind CPSs, develop models and controls, identify safety specifications and critical properties of CPSs, understand abstraction and system architectures, learn how to design by invariant, reason rigorously about CPS models, verify CPS models of appropriate scale, understand the semantics of a CPS model and develop an intuition for operational effects. Students will write hybrid programs (HPs), which capture relevant dynamical aspects of CPSs in a simple programming language with a simple semantics, allowing the programmer to refer to real-valued variables representing real quantities and specify their dynamics as part of the HP.

Prerequisites: 15-122 and 21-122 and (15-251 or 21-241 or 18-202)

Course Website: http://symbolaris.com/course/fcps16.html

- 15-437 Web Application Development
- Fall and Spring: 12 units

This course will introduce concepts in programming web application servers. We will study the fundamental architectural elements of programming web sites that produce content dynamically. The primary technologies introduced will be the Django framework for Python and Java Servlets, but we will cover related topics as necessary so that students can build significant applications. Such topics include: HTTP, HTML, CSS, Javascript, XML, Design Patterns, Relational and Non-relational Databases, Object-Relation Mapping tools, Security, Web Services, Cloud Deployment, Internationalization, and Scalability and Performance Issues. Students must be comfortable programming in Java and/or Python to register for this course. Students must provide their own computer hardware for this course. Please see the Related URL above for more information.

Prerequisite: 15-214

Course Website: http://www.jeffeppinger.com/jle/web-app.html

- 15-440 Distributed Systems
- Fall and Spring: 12 units

The goals of this course are twofold: First, for students to gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving. As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects. As a consequence, competency in both the C and Java programming languages is required.

Prerequisite: 15-213

- 15-441 Computer Networks
- Fall and Spring: 12 units

The emphasis in this course will be on the basic performance and engineering trade-offs in the design and implementation of computer networks. To make the issues more concrete, the class includes several multi-week projects requiring significant design and implementation. The goal is for students to learn not only what computer networks are and how they work today, but also why they are designed the way they are and how they are likely to evolve in the future. We will draw examples primarily from the Internet. Topics to be covered include: network architecture, routing, congestion/flow/error control, naming and addressing, peer-to-peer and the web, internetworking, and network security.

Prerequisite: 15-213

- 15-449 Engineering Distributed Systems
- Spring: 9 units

This is a course for students with strong design and implementation skills who are likely to pursue careers as software architects and lead engineers. It may be taken by well-prepared undergraduates with excellent design and implementation skills in low-level systems programing. The course assumes a high level of proficiency in all aspects of operating system design and implementation. This course will help students prepare for leadership roles in creating and evolving the complex, large-scale computer systems that society will increasingly depend on in the future. The course will teach the organizing principles of such systems, identifying a core set of versatile techniques that are applicable across many system layers. Students will acquire the knowledge base, intellectual tools, hands-on skills and modes of thought needed to build well-engineered computer systems that withstand the test of time, growth in scale, and stresses of live use. Topics covered include: caching, prefetching, damage containment, scale reduction, hints, replication, hash-based techniques, and fragmentation reduction. A substantial project component is an integral part of the course. A high level of proficiency in systems programming is expected. If you do not have the 15-410 prerequisite you will need to get approval from the faculty.

Prerequisite: 15-410

- 15-451 Algorithm Design and Analysis
- Fall and Spring: 12 units

This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols.

Prerequisites: 21-241 and 15-251 and 15-210

- 15-453 Formal Languages, Automata, and Computability
- Intermittent: 9 units

An introduction to the fundamental ideas and models underlying computing: finite automata, regular sets, pushdown automata, context-free grammars, Turing machines, undecidability, and complexity theory.

Prerequisites: 15-251 or 21-228

- 15-455 Undergraduate Complexity Theory
- Intermittent: 9 units

Complexity theory is the study of how much of a resource (such as time, space, parallelism, or randomness) is required to perform some of the computations that interest us the most. In a standard algorithms course, one concentrates on giving resource efficient methods to solve interesting problems. In this course, we concentrate on techniques that prove or suggest that there are no efficient methods to solve many important problems. We will develop the theory of various complexity classes, such as P, NP, co-NP, PH, #P, PSPACE, NC, AC, L, NL, UP, RP, BPP, IP, and PCP. We will study techniques to classify problems according to our available taxonomy. By developing a subtle pattern of reductions between classes we will suggest an (as yet unproven!) picture of how by using limited amounts of various resources, we limit our computational power.

Prerequisite: 15-251

- 15-456 Computational Geometry
- Intermittent: 9 units

How do you sort points in space? What does it even mean? This course takes the ideas of a traditional algorithms course, sorting, searching, selecting, graphs, and optimization, and extends them to problems on geometric inputs. We will cover many classical geometric constructions and novel algorithmic methods. Some of the topics to be covered are convex hulls, Delaunay triangulations, graph drawing, point location, geometric medians, polytopes, configuration spaces, linear programming, and others. This course is a natural extension to 15-451, for those who want to learn about algorithmic problems in higher dimensions.

Prerequisite: 15-451

- 15-457 Special Topics in Theory: Advanced Algorithms
- Intermittent: 12 units

Selected advanced topics in algorithms and computational theory. Topics vary from semester to semester.

Prerequisite: 15-451

- 15-462 Computer Graphics
- Fall and Spring: 12 units

This course provides a comprehensive introduction to computer graphics modeling, animation, and rendering. Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing.

Prerequisites: (21-259 and 15-213 and 21-240) or (21-241 and 21-259 and 15-213) or (18-213 and 18-202)

- 15-463 Computational Photography
- Fall: 12 units

Computational Photography is an emerging new field created by the convergence of computer graphics, computer vision and photography. Its role is to overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of our visual world. The aim of this advanced undergraduate course is to study ways in which samples from the real world (images and video) can be used to generate compelling computer graphics imagery. We will learn how to acquire, represent, and render scenes from digitized photographs. Several popular image-based algorithms will be presented, with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming assignments, in which students will have the opportunity to acquire their own images of indoor and outdoor scenes and develop the image analysis and synthesis tools needed to render and view the scenes on the computer.

Prerequisites: 15-213 and (18-202 or 21-241)

Course Website: http://graphics.cs.cmu.edu/courses/15-463/

- 15-464 Technical Animation
- Fall: 12 units

This course introduces techniques for computer animation such as keyframing, procedural methods, motion capture, and simulation. The course also includes a brief overview of story-boarding, scene composition, lighting and sound track generation. The second half of the course will explore current research topics in computer animation such as dynamic simulation of flexible and rigid objects,automatically generated control systems, and evolution of behaviors. The course should be appropriate for graduate students in all areas and for advanced undergraduates.

Prerequisite: 15-462

- 15-465 Animation Art and Technology
- Spring: 12 units

Animation Art and Technology is an interdisciplinary course cross-listed between Art and Computer Science. Faculty and teaching assistants from computer science and art teach the class as a team. It is a project-based course in which four to five interdisciplinary teams of students produce animations. Most of the animations have a substantive technical component and the students are challenged to consider innovation with content to be equal with the technical. The class includes basic tutorials for work in Maya leading toward more advanced applications and extensions of the software such as motion capture and algorithms for animating cloth, hair, particles, and grouping behaviors. The first class will meet in CFA room 303.

Prerequisites: 18-213 or 15-213

- 15-466 Computer Game Programming
- Spring: 12 units

The goal of the course is two-fold. The first goal is to teach students some of the higher-level techniques that are necessary to implement interesting computer games. In particular, a large emphasis will be on game AI. The class will also cover such topics as game networking, scheduling of tasks in computationally intensive games, and game design. The class will also briefly cover few selected topics such as collision detection and physically-based animation that are good to know even if one is developing games using a game engine. The second goal of the class is to get students familiar with programming games on various platforms using state-of-the-art game engines. To this end, the course will have a heavy programming focus. It will have a number of projects requiring students to apply the learned material to develop games using Unity 3D game engine. The students will develop games that run on a standalone PC, games that run on an Android platform and games that work in a multi-user mode. In addition to learning the material and learning how to use game engines, the projects will provide students with portfolios of games they have developed. Such a portfolio is important in getting a job in game industry. Prerequisite is 15-462 or contact the instructor for permission.

Prerequisite: 15-462

- 15-487 Introduction to Computer & Network Security & Applied Cryptography
- Fall: 12 units

This course will introduce students to the fundamentals of computer security and applied cryptography. Topics include software security, networking and wireless security, and applied cryptography. Students will also learn the fundamental methodology for how to design and analyze security critical systems.

Prerequisite: 15-213

- 15-491 Special Topic: CMRoboBits: AI and Robots for Daily-Life Problems
- Fall: 12 units

This course will be a project-based course in which we will look at AI and robotics artifacts and techniques to automate solutions to real-world problems, in particular related to life in cities. The course will start by collecting and brainstorming about real problems biased to ones that involve the physical space in addition to the cyber information space, such as traffic rush hour, noise in cities, 3D building inspection, service and data gathering. We will then formalize the chosen problems and analyze existing real data. The course will proceed by possibly enabling the students to prototype their projects beyond simulation, and using the CORAL lab robots, e.g., the CoBot or NAO robots or drones. The course work will be a single large project, performed by groups of up to 3 students. The projects will be divided in three phases, due at the end of February, March, and the end of the course. Students are expected to have programming experience in C++ or python.

Prerequisite: 15-122

- 15-492 Special Topic: Speech Processing
- Fall: 12 units

Speech Processing offers a practical and theoretical understanding of how human speech can be processed by computers. It covers speech recognition, speech synthesis and spoken dialog systems. The course involves practicals where the student will build working speech recognition systems, build their own synthetic voice and build a complete telephone spoken dialog system. This work will be based on existing toolkits. Details of algorithms, techniques and limitations of state of the art speech systems will also be presented. This course is designed for students wishing understand how to process real data for real applications, applying statistical and machine learning techniques as well as working with limitations in the technology.

Prerequisite: 15-122

Course Website: http://www.speech.cs.cmu.edu/15-492/

- 15-494 Special Topic: Cognitive Robotics
- Spring: 12 units

Cognitive robotics is a new approach to robot programming based on high level primitives for perception and action. These primitives draw inspiration from ideas in cognitive science combined with state of the art robotics algorithms. Students will experiment with these primitives and help develop new ones using the Tekkotsu software framework on the Calliope robot, which includes a 5 degree-of-freedom arm with gripper, a Kinect camera on a pan/tilt mount, and Ubuntu Linux on a dual-core on-board netbook. Prior robotics experience is not necessary, but strong programming skills are required.

Prerequisite: 15-213

- 15-591 Independent Study in Computer Science
- Fall and Spring

The School of Computer Science offers Independent Study courses, which allow motivated students to work on projects under the supervision of a faculty advisor while receiving academic credit. Independent studies are usually one semester in duration and require prior approval from the faculty member and the School of Computer Science.

- 15-592 Independent Study in Computer Science
- Fall

to be determined

- 15-593 Independent Study in Computer Science
- Fall

To be determined

- 15-594 Independent Study in Computer Science
- Fall

to be determined

- 15-599 SCS Honors Undergraduate Research Thesis
- Fall and Spring

Available only to students registered in the CS Senior Research Thesis Program.

- 15-610 Engineering Distributed Systems
- Spring: 12 units

This is a master's level course for students with strong design and implementation skills who are likely to pursue careers as software architects and lead engineers. It may be taken by well-prepared undergraduates with excellent design and implementation skills in low-level systems programing. The course assumes a high level of proficiency in all aspects of operating system design and implementation. This course will help students prepare for leadership roles in creating and evolving the complex, large-scale computer systems that society will increasingly depend on in the future. The course will teach the organizing principles of such systems, identifying a core set of versatile techniques that are applicable across many system layers. Students will acquire the knowledge base, intellectual tools, hands-on skills and modes of thought needed to build well-engineered computer systems that withstand the test of time, growth in scale, and stresses of live use. Topics covered include: caching, prefetching, damage containment, scale reduction, hints, replication, hash-based techniques, and fragmentation reduction. A substantial project component is an integral part of the course. A high level of proficiency in systems programming is expected.

Prerequisite: 15-410

Course Website: http://www.cs.cmu.edu/~15-610

- 15-611 Compiler Design
- Fall: 15 units

This course covers the design and implementation of compiler and run-time systems for high-level languages, and examines the interaction between language design, compiler design, and run-time organization. Topics covered include syntactic and lexical analysis, handling of user-defined types and type-checking, context analysis, code generation and optimization, and memory management and run-time organization. Prerequisites: 15213 or 15312

Course Website: http://symbolaris.com/course/compiler12.html

- 15-688 Practical Data Science
- Fall: 12 units

Data science is the study and practice of how we can extract insight and knowledge from large amounts of data. This course provides a practical introduction to the "full stack" of data science analysis, including data collection and processing, data visualization and presentation, statistical model building using machine learning, and big data techniques for scaling these methods. Topics covered include: collecting and processing data using relational methods, time series approaches, graph and network models, free text analysis, and spatial geographic methods; analyzing the data using a variety of statistical and machine learning methods include linear and non-linear regression and classification, unsupervised learning and anomaly detection, plus advanced machine learning methods like kernel approaches, boosting, or deep learning; visualizing and presenting data, particularly focusing the case of high-dimensional data; and applying these methods to big data settings, where multiple machines and distributed computation are needed to fully leverage the data.Students will complete weekly programming homework that emphasize practical understanding of the methods described in the course. In addition, students will develop a tutorial on an advanced topic, and will complete a group project that applies these data science techniques to a practical application chosen by the team; these two longer assignments will be done in lieu of a midterm or final.

Course Website: http://www.datasciencecourse.org

- 15-705 Engineering Distributed Systems
- Spring: 12 units

This course is for students with strong design and implementation skills who are likely to pursue careers as software architects and lead engineers. It may be taken by well-prepared undergraduates with excellent design and implementation skills in low-level systems programing. The course assumes a high level of proficiency in all aspects of operating system design and implementation. This course will help students prepare for leadership roles in creating and evolving the complex, large-scale computer systems that society will increasingly depend on in the future. The course will teach the organizing principles of such systems, identifying a core set of versatile techniques that are applicable across many system layers. Students will acquire the knowledge base, intellectual tools, hands-on skills and modes of thought needed to build well-engineered computer systems that withstand the test of time, growth in scale, and stresses of live use. Topics covered include: caching, prefetching, damage containment, scale reduction, hints, replication, hash-based techniques, and fragmentation reduction. A substantial project component is an integral part of the course. A high level of proficiency in systems programming is expected. Please refer to http://www.cs.cmu.edu/~csd-grad/courseschedules14.html this link for the most recent schedule updates.

Course Website: http://www.cs.cmu.edu/~15-705

- 15-719 Advanced Cloud Computing
- Spring: 12 units

Computing in the cloud has emerged as a leading paradigm for cost-effective, scalable, well-managed computing. Â Users pay for services provided in a broadly shared, power efficient datacenter, enabling dynamic computing needs to be met without paying for more than is needed. Â Actual machines may be virtualized into machine-like services, or more abstract programming platforms, or application-specific services, with the cloud computing infrastructure managing sharing, scheduling, reliability, availability, elasticity, privacy, provisioning and geographic replication. Please refer to https://www.cs.cmu.edu/~csd-grad/courseschedules16.html for the most recent schedule updates.

Prerequisites: 15-513 or 18-213 or 15-213

Course Website: http://www.cs.cmu.edu/~15719/

- 15-749 Engineering Distributed Systems
- Fall: 12 units

Computing has changed beyond recognition in half a century, from the room-filling mainframes of the 1960s to today's smartphones and wearable devices. Networks have also changed dramatically: from the 300-baud dialup modems of the early networking era to gigabit LANs, Wi-Fi and 4G today. Who knows what changes are in store for us over the next half century? Astonishingly, in spite of this tremendous change in hardware technology over time, a small core set of techniques for building distributed systems has emerged and remained surprisingly stable and applicable across many system layers. Many flavors of these techniques exist, and they continously evolve over time to reflect changing trade-offs in the design space. What are these core techniques, and how can we leverage them in creating distributed systems today and in the future? That is the central question addressed by this course. Students will acquire the knowledge base, intellectual tools, hands-on skills and modes of thought needed to build well-engineered distributed systems that withstand the test of time, growth in scale, and stresses of live use. Strong design and implementation skills are expected of all students. The course assumes a high level of proficiency in all aspects of operating system design and implementation. A substantial project component is an integral part of the course. Please refer to http://www.cs.cmu.edu/~csd-grad/courseschedules14.html this link for the most recent schedule updates.

Course Website: http://www.cs.cmu.edu/~15-749/

- 15-751 A Theorist's Toolkit
- Intermittent: 12 units

Course description: This course will take a random walk through various mathematical topics that come in handy for theoretical computer science. It is intended mainly for students earlier in their graduate studies (or very strong undergraduates) who want to do theory research. The idea for the course comes from other courses by Arora (2002, 2007), Håstad (2004/05), Kelner (2007, 2009), and Tulsiani (2013). Prerequisites Students should have a solid undergraduate background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (running time analysis, big-O/Omega/Theta, P and NP, basic fundamental algorithms). Mathematical maturity is a must. Prerequisites Students should have a solid undergraduate background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (running time analysis, big-O/Omega/Theta, P and NP, basic fundamental algorithms). Mathematical maturity is a must. https://www.cs.cmu.edu/~csd-grad/courseschedulef16.html CLASSES WILL BEGIN WEEK OF SEPTEMBER 5th.

Course Website: http://www.cs.cmu.edu/~odonnell/toolkit16/

- 15-769 Visual Computing Systems
- Intermittent: 12 units

DESCRIPTION:Visual computing tasks such as 2D/3D graphics, image processing, and image understanding are important responsibilities of modern computer systems ranging from sensor-rich smart phones to large datacenters. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel (and heterogeneous) systems that serve to accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient future graphics and image processing platforms and for students seeking to develop scalable algorithms for these platforms. https://www.cs.cmu.edu/~csd-grad/courseschedulef16.html CLASS WILL BEGIN THE WEEK OF SEPTEMBER 5th.

Course Website: http://graphics.cs.cmu.edu/courses/15869/fall2014/

- 15-883 Computational Models of Neural Systems
- Intermittent: 12 units

This course is an in-depth study of information processing in real neural systems from a computer science perspective. We will examine several brain areas, such as the hippocampus and cerebellum, where processing is sufficiently well understood that it can be discussed in terms of specific representations and algorithms. We will focus primarily on computer models of these systems, after establishing the necessary anatomical, physiological, and psychophysical context. There will be some neuroscience tutorial lectures for those with no prior background in this area. Please refer to http://www.cs.cmu.edu/~csd-grad/courseschedulef15.html for the most recent schedule updates.

Course Website: http://www.cs.cmu.edu/afs/cs/academic/class/15883-f13/

- 15-897 Advanced Topics in Parallel Computing:
- Spring: 12 units

Section A: In this special topics course on parallel computing, the students will learn about the foundations of parallel computing and complete a research project. The course will cover both theory and practice. The topics covered will include basic parallel algorithms for sequences (e.g., mapping, reductions), trees (e.g., tree contraction), and graphs (graph search, connectivity, graph contraction) as well as scheduling algorithms (e.g., work stealing) for mapping parallel computation to hardware for optimal efficiency and performance. There will be one midterm, several assignments (involving programming), and one project; the project will account for 50% of the grade. ** Project Ideas ** The project can be theoretical or practical. The ideal project would be one that adds parallelism to the students' current research. For example, if the student does research in a field such as algorithms/graphics/robotics and he or she can design/analyze/implement a parallel algorithm (possibly for an open problem); if the student does research on a field such as programming languages or databases, she or he can design/implement language/query support for parallelism; if the student does research on machine learning, he or she can develop a framework for performing approximate inference in parallel. Projects can be done individually or in groups and will lead to a small paper. We envision the students in the class to review other project papers (as in a program committee) and provide feedback to the authors. Please refer to https://www.cs.cmu.edu/~csd-grad/courseschedules16.html for the most recent schedule updates.