SCS Additional Minors

This page lists Additional Majors and Minors apart from those in Artificial Intelligence, Computational BiologyComputer Science , Human-Computer Interaction and Robotics. Select from the tabs below to view more information about each program.

Students should consult with their own academic advisor as well as the advisor for the given minor for specific double-counting rules, especially for students who are pursuing an SCS minor with a major or other minors closely related to computing. Additional help can be provided by the Associate Dean in the Computer Science Undergraduate Program office (Gates-Hillman Center, 4th Floor).

A note on SCS Concentrations: Computer Science majors are required to pursue a minor outside of SCS or a concentration within SCS. Additional majors in SCS are still allowed for Computer Science majors. Artificial Intelligence, Computational Biology, Human-Computer Interaction and Robotics majors can complete an SCS concentration if they wish, but it is not required for these degrees. Minors in SCS will not be allowed for SCS students where there is an aligned concentration. For example, an SCS student cannot minor in Machine Learning since there is a Machine Learning concentration. Consult the SCS Concentrations section for details on available SCS concentrations.

IDeATe Minors

Kelly Delaney, Advisor
kellydel@andrew.cmu.edu
https://ideate.cmu.edu 

The Integrative Design, Arts and Technology (IDeATe) network offers students the opportunity to become immersed in a collaborative community of faculty and peers who share expertise, experience, and passions at the intersection of arts and technology. Students engage in active "learning by doing" in state-of-the-art maker spaces. The program addresses current and emerging real-world challenges that require disciplinary expertise coupled with multidisciplinary perspectives and collaborative integrative approaches.

The IDeATe undergraduate curriculum consists of ten areas, all of which can be taken as minors. The themes of these areas integrate knowledge in technology and the arts. Five of these minors are based in the School of Computer Science:

Animation & Special Effects Minor

Animation & Special Effects comprise a rich field of inquiry at the intersection of art, science, and technology. Students in the IDeATe Animation & Special Effects minor will gain experience and competency across a wide range of techniques, while learning about the diverse histories, theories, and practices of animation from renowned faculty experts and visiting artists. Coursework cultivates development of unique aesthetics and individual voice through opportunities for group critique, iteration, public screening and exhibition. Through the minor, students will: have opportunities to collaborate and connect with peers in other fields of research; develop relevant practical skills and abilities that can be applied across a variety of independent studio and industry contexts; deepen cultural sensitivities while expanding their own creative practices; and develop a compelling animation portfolio. In particular, students will gain skills and competencies in the following areas:

  • Storytelling through animation
  • Digital 2D and 3D animation techniques
  • Expanded and experimental animation methods
  • Real-time animation systems
  • Motion-capture technologies
  • Visual effects and procedural animation
  • Rendering and compositing

Curriculum

One Computing Course - Minimum of 9 Units
Units
15-104Introduction to Computing for Creative Practice10
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
60-212Intermediate Studio: Creative Coding12
One IDeATe Portal Course - Minimum of 9 Units
Units
60-125IDeATe: Introduction to 3D Animation Pipeline
Recommended portal course for this area
12
16-223IDeATe Portal: Creative Kinetic Systems10
18-090Twisted Signals: Multimedia Processing for the Arts10
53-322IDeATe: Little Games/Big Stories: Indie Roleplaying Game Studio9
60-223IDeATe Portal: Introduction to Physical Computing10
62-150IDeATe Portal: Introduction to Media Synthesis and Analysis10
82-250Digital Realities: Introducing Immersive Technologies for Arts and Culture9
99-361IDeATe Portal9
IDeATe Animation & Special Effects Courses - Minimum of 27 Units
Units
15-463Computational Photography12
15-465/60-414Animation Art and Technology12
53-320IDeATe Special Topics in Animation: Character Modeling6
53-321IDeATe Special Topics in Animation: Bipedal Rigging for Animation Production6
53-323IDEATE Storytelling Through Effects Animation6
60-220IDeATe: Technical Character Animation10
60-333IDeATe: Animation Rigging10
60-335IDeATe Special Topics in Animation: Story Development6
60-398Critical Studies: Social History of Animation9
60-413Advanced ETB: Real-Time Animation10
60-415Advanced ETB: Animation Studio10
60-417Advanced ETB: Video Art10
Additional course options as available. Please refer to the IDeATe website for courses for the current and upcoming semester. 
Double-Counting

Students may double-count up to two of their Animation & Special Effects minor courses toward other requirements.

Intelligent Environments Minor

Students in the Intelligent Environments minor are concerned with the design and realization of interactive 3D spaces, both physical and virtual.  Students in this minor can explore how information and energy flow between physical, electronic, and computational spaces. By moving through space and time, we make sense of the world using our bodies. Just as we shape the environments around us, they in turn shape our experiences and senses, making us mindful of the need to develop responsible, equitable and inclusive environments. As a student in Intelligent Environments, through experimentation, hands-on learning, reflection, and documentation, you will learn:

  • Analytical skills for the visualization and realization interactive spaces
  • Principles of multimodal and embodied interactions
  • 3-dimensional computer-aided design (CAD) for visualization, simulation, and fabrication
  • The cultural context, and social and environmental implications of constructed environments

Students in this minor work in tandem with the Physical Computing and Media Design areas, which provide knowledge in key component elements of integrative intelligent environments. Accordingly, students can customize their studies by combining courses across these three concentrations with the help of their advisors.

Curriculum

One Computing Course - Minimum of 9 Units
Units
15-104Introduction to Computing for Creative Practice10
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
60-212Intermediate Studio: Creative Coding12
One IDeATe Portal Course - Minimum of 9 Units
Units
16-223IDeATe Portal: Creative Kinetic Systems
Recommended Portal Course for this area
10
60-223IDeATe Portal: Introduction to Physical Computing
Recommended Portal Course for this area
10
18-090Twisted Signals: Multimedia Processing for the Arts10
53-322IDeATe: Little Games/Big Stories: Indie Roleplaying Game Studio9
60-125IDeATe: Introduction to 3D Animation Pipeline12
62-150IDeATe Portal: Introduction to Media Synthesis and Analysis10
82-250Digital Realities: Introducing Immersive Technologies for Arts and Culture9
99-361IDeATe Portal9
IDeATe Intelligent Environments Courses - Minimum of 27 Units
Units
05-333Gadgets, Sensors and Activity Recognition in HCI12
16/54-375IDeATe: Robotics for Creative Practice10
16-376IDeATe: Kinetic Fabrics10
16-467Human Robot Interaction12
18/05-540Rapid Prototyping of Computer Systems12
48-528IDeATe: Responsive Mobile Environments9
51-361HyperSENSE: Augmenting Human Experience in Environments9
53-558Reality Computing Studio12
99-362IDeATe: Intelligent Learning Spaces9
Additional course options as available. Please refer to the IDeATe website for courses for the current and upcoming semester. 
Double-Counting

Students may double-count up to two of their Intelligent Environments minor courses toward other majors and minors.

Design for Learning Minor

Students in the Design for Learning minor, offered by the Human-Computer Interaction Institute (HCII), combine skills to imagine, design, iterate, and evaluate effective new media systems for learning—from creating games for learning to integrating adaptive ed-tech and augmented reality experiences into diverse learning settings. In team-based collaborations, students focus on the critical design of learning platforms, products, and systems that leverage emerging technologies, learning science research, inclusive design, and data analytics to create engaging educational experiences with measurable real-world impact.

Through coursework in the minor, you will gain skills and competencies in: 

  • Learning design research and evaluation methods
  • Concept modeling and prototyping techniques
  • Learner-centered, inclusive and backward design frameworks
  • Applied learning research and theory in team-based projects
  • Communicating design choices and concepts to diverse stakeholders

Students in Design for Learning courses can bring media-making and prototyping competencies gained in other IDeATe areas (e.g. Game Design, Media Design, Physical Computing, Immersive Technologies in Arts & Humanities) to craft innovative learning experiences.

Curriculum

One Computing Course - Minimum of 9 Units
Units
15-104Introduction to Computing for Creative Practice10
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
60-212Intermediate Studio: Creative Coding12
One IDeATe Portal Course - Minimum of 9 Units
Units
62-150IDeATe Portal: Introduction to Media Synthesis and Analysis
Recommended Portal Course for this area
10
99-361IDeATe Portal
Recommended Portal Course for this area
9
16-223IDeATe Portal: Creative Kinetic Systems10
18-090Twisted Signals: Multimedia Processing for the Arts10
53-322IDeATe: Little Games/Big Stories: Indie Roleplaying Game Studio9
60-125IDeATe: Introduction to 3D Animation Pipeline12
60-223IDeATe Portal: Introduction to Physical Computing10
82-250Digital Realities: Introducing Immersive Technologies for Arts and Culture9
IDeATe Design for Learning Courses - Minimum of 27 Units
05-291Learning Media Design12
05-292IDeATe: Learning in Museums12
05-321Transformational Game Design Studio12
05-418Design Educational Games12
05-432Personalized Online Learning12
05-738Evidence-Based Educational Design12
05-823E-Learning Design Principles and Methods12
51-486Designing Experiences for Learning9
79-343Education, Democracy, and Civil Rights9
82-288Everyday Learning: Designing Learning Exp in Times of Unrest & UncertaintyVar.
90-463Policy and Leadership in Public Education6
99-362IDeATe: Intelligent Learning Spaces9
Additional course options as available. Please refer to the IDeATe website for courses for the current and upcoming semester. 
Double-Counting

Students may double-count up to two of their Design for Learning minor courses toward requirements for other majors and minors.

Physical Computing Minor

Physical computing is driven by a creative combination of arts and engineering disciplines. Our students’ projects interact with their surroundings, remember information, make decisions, and generate tangible outputs like movement, sound, or light. Physical computing projects range from the tiny and plain (a blinking light on a breadboard) to the extravagant (a simulation of an alien landscape), and everything in between. They may be functional, like an assistive device for a person with disability, playful, like an interactive marble run, or exploratory, like a prototype for a future human-computer interface in a world of sentient machines.

Students gain a broad range of skills in our courses because physical computing as a field is fundamentally interdisciplinary: our projects combine software, electronics, and physical fabrication. Students in the Physical Computing minor learn how to:

  • Write low-level software to computationally define a project’s behavior, usually using C or Python
  • Fabricate projects using techniques borrowed from various crafts and disciplines, such as making simple assemblies with paper and tape; woodworking for larger or more robust projects; textile/fabric integrations; and creating powered mechanical linkages using motors/gears/belts/bearings/etc.
  • Design, test, assemble, and debug electronic circuits to bring a project to life
  • Use 3-dimensional computer-aided design (CAD) for visualization, simulation, and fabrication of all of the above
  • Combine digital fabrication techniques (3D printing, laser cutting, etc.) with hand craft to iterate towards creating a final, polished product

Curriculum

One Computing Course - Minimum of 9 Units
Units
15-104Introduction to Computing for Creative Practice10
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
60-212Intermediate Studio: Creative Coding12
One IDeATe Portal Course - Minimum of 9 Units
Units
16-223IDeATe Portal: Creative Kinetic Systems
Recommended Portal Course for this area
10
60-223IDeATe Portal: Introduction to Physical Computing
Recommended Portal Course for this area
10
18-090Twisted Signals: Multimedia Processing for the Arts10
53-322IDeATe: Little Games/Big Stories: Indie Roleplaying Game Studio9
60-125IDeATe: Introduction to 3D Animation Pipeline12
62-150IDeATe Portal: Introduction to Media Synthesis and Analysis10
82-250Digital Realities: Introducing Immersive Technologies for Arts and Culture9
99-361IDeATe Portal9
IDeATe Physical Computing Courses - Minimum of 27 Units
Units
05-333Gadgets, Sensors and Activity Recognition in HCI12
05/18-540Rapid Prototyping of Computer Systems12
15-294Special Topic: Rapid Prototyping Technologies5
15-394Intermediate Rapid Prototyping5
16/54-375IDeATe: Robotics for Creative Practice10
16-376IDeATe: Kinetic Fabrics10
16-480IDeATe: Special Topics: Creative Soft Robotics10
18/05-540Rapid Prototyping of Computer Systems12
18-578Mechatronic Design12
24-672Special Topics in DIY Design and Fabrication12
39-245Rapid Prototype Design9
48-528IDeATe: Responsive Mobile Environments9
62-362IDeATe: Electronic Logics && Creative Practice12
62-478IDeATe: digiTOOL9
Additional course options as available. Please refer to the IDeATe website for courses for the current and upcoming semester. 
Double-Counting

Students may double-count up to two of their Physical Computing minor courses toward requirements for other majors and minors.

Soft Technologies Minor

Soft technologies is an emerging field of robotics, the arts, craft, and engineering with far-reaching commercial, research, and social implications. Individual disciplines address components of this burgeoning field, but the IDeATe Soft Technologies minor helps students integrate the pieces to be able to make significant contributions to this developing sphere. Through the courses in the minor, students weave together a rich set of established and experimental techniques in traditional soft materials (such as fibers and textiles) and new soft materials (such as current hybrid and dynamic materials) to design and create a variety of forms with applications ranging from novel to practical. Students explore the unique qualities that soft material technologies afford in design and interaction in relationship to environments and the human body— responsiveness, adaptivity, flexibility, sensitivity, morphing, and biomimicry. Students will engage in project-based inquiry, using research, experimentation, making, and reflection to inform their creativity and to develop critical perspectives. Students will be able to envision their own projects and develop sensitivities to the breadth and limitations of soft technologies.

Through coursework in the minor, you will gain skills and competencies in: 

  • Manipulating traditional soft materials (such as fibers and textiles) and new soft materials (such as current  hybrid and dynamic materials).
  • Constructing 3-dimensional forms from 2-dimensional planes.
  • Articulating material and conceptual choices in discussions and critiques.
  • Analyzing the relationships between materials, form, use, and content integral to making.
  • Researching and engaging with contemporary and/or historical precedents in the field

Curriculum

One Computing Course - Minimum of 9 Units
Units
15-104Introduction to Computing for Creative Practice10
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
60-212Intermediate Studio: Creative Coding12
One IDeATe Portal Course - Minimum of 9 Units
Units
62-150IDeATe Portal: Introduction to Media Synthesis and Analysis
Recommended Portal Course for this area
10
99-361IDeATe Portal
Recommended Portal Course for this area
9
16-223IDeATe Portal: Creative Kinetic Systems10
18-090Twisted Signals: Multimedia Processing for the Arts10
53-322IDeATe: Little Games/Big Stories: Indie Roleplaying Game Studio9
60-125IDeATe: Introduction to 3D Animation Pipeline12
60-223IDeATe Portal: Introduction to Physical Computing10
82-250Digital Realities: Introducing Immersive Technologies for Arts and Culture9
IDeATe Soft Technologies Courses - Minimum of 27 Units
Units
09-227The Culture of Color: Dyes, Chemistry, and Sustainability9
15-367Algorithmic Textiles Design12
16-224IDeATe: Re-Crafting Computational Thinking with Soft Technologies6
16-376IDeATe: Kinetic Fabrics10
27-505Exploration of Everyday Materials9
54-346Introduction to Costume Construction6
54-486Understanding Textiles3
Additional course options as available. Please refer to the IDeATe website for courses for the current and upcoming semester. 
Double-Counting

Students may double-count up to two of their Soft Technologies minor courses toward requirements for other majors and minors.

Information Security, Privacy, and Policy Minor

Lujo Bauer, Director

There is a growing demand for security and privacy experts, and increasing interest among CMU undergraduates in taking security and privacy courses. Security and privacy expertise is an asset in a variety of careers outside, not just in computer science, but also in areas that include business, management, and law. In addition, the policy side of security and privacy is becoming increasingly important and employers are interested in hiring people with an understanding of relevant policy issues, especially in the privacy and security area.

This minor is for undergraduate students across the university who are interested in policy issues related to security and privacy, including those who are planning careers in security/privacy as well as those who plan to focus their careers in other areas. The curriculum has been designed to accommodate students from any major as long as they have taken at least one introductory-level college programming course (such as 15-110 or 15-112).

After completing this minor, students will have a good understanding of how to identify potential security and privacy risks and relevant legal and policy issues; a working understanding of security topics such as cryptography, authentication, and Internet security protocols; as well as broad knowledge of several security- and privacy-related areas as they pertain to the design, development, deployment and management of technologies in a variety of practical contexts (e.g., Web, mobile, Internet of Things, social media, crypto currencies).

Admission

Students are not required to apply to enroll in this minor to start the required courses. However, students should declare their intent to complete the minor and submit a planned course of study to the minor director, and are encouraged to consult with the minor director on their elective course selection. In addition, students doing the independent study option must get approval from the minor director prior to enrolling in their independent study course. Finally, students must contact the minor director to certify their completion of the minor.

Curriculum

Students are required to take five courses to complete this minor with a minimum of 48 units.

INTRODUCTORY SECURITY COURSE

17-331Information Security, Privacy, and Policy12

Students who have taken 15-213 Introduction to Computer Systems may substitute 15-330 Introduction to Computer Security/18-330 Introduction to Computer Security

PRIVACY AND POLICY COURSE

17-333Privacy Policy, Law, and Technology9

Students may substitute 12-unit version of this course: 17-733, 19-608, or 95-818.

PRIVACY ELECTIVE

Complete a minimum of 9 units: Units
17-334Usable Privacy and Security
(or 19-534 or 05-436)
9
17-702Current Topics in Privacy Seminar
(3-unit Mini)
3
17-731Foundations of Privacy12
17-735Engineering Privacy in Software12
17-880Algorithms for Private Data Analysis12
94-806Privacy in the Digital Age6

Crosslisted courses are also allowed.

TECHNOLOGY AND POLICY ELECTIVE

Complete a minimum of 9 units: Units
17-200Ethics and Policy Issues in Computing9
19-211Ethics and Policy Issues in Computing9
17-562Law of Computer Technology9
19-101Introduction to Engineering and Public Policy12
19-402Telecommunications Technology and Policy for the Internet Age12
19-403Policies of Wireless Systems12
19-639Policies of the Internet12
84-387Remote Systems and the Cyber Domain in Conflict9

Crosslisted courses are also allowed.

ADDITIONAL APPROVED ELECTIVE

Students must complete an additional elective of 9 units or more. Students may choose an additional privacy elective or technology policy elective from the list above, or the one of the following security electives:

15-316Software Foundations of Security and Privacy9
15-356Introduction to Cryptography12
17-303Cryptocurrencies, Blockchains and ApplicationsVar.
17-334Usable Privacy and Security9
18-335Secure Software Systems12
18-733Applied Cryptography
18-435Foundations of Blockchains12
18-334Network Security12

Students who have the necessary prerequisites may choose any approved elective from the SCS or ECE security and privacy undergraduate concentration. Check with the minor program director to determine which category of elective each course will fulfill.

Students should be careful to choose electives for which they have appropriate prerequisites. New elective options are expected as more courses are offered. Students may petition to count a course not on this list as an elective. Students should request permission before taking a course that is not on this list. Students may not count multiple electives that overlap substantially.

Optional Project: Subject to approval by the minor director, students may optionally count towards one of the elective requirements 9 units of an independent study or research project course in the security or privacy area, under the supervision of a faculty member in any department.  In order to receive credit towards the minor, students must submit a brief project proposal to their project advisor and to the minor director and have it approved prior to conducting the project. Depending on the topic of the project, the minor director may approve credits counting towards privacy electives, technology policy electives, security electives, or some combination of these. Students may work individually, with other undergraduates, or as part of project teams with graduate students or research staff. Students involved in a group project must identify specific project components for which they are responsible. In addition, they must submit a final project report to their project advisor and the minor director that includes a literature review and describes the work they completed. Students working on a group project must each submit their own final report, which should also situate their contribution in the context of the larger project. Note, students are expected to work approximately 1 hour per week for each unit of project in which they are enrolled (e.g. 9 units = 9 hours/week of project work).

Double Counting: At most 2 of the courses used to fulfill the minor requirements may be counted towards any other undergraduate major or minor program. This rule does not apply to courses counted for general education requirements.

Language Technologies Minor

Carolyn P. Rose, Chair
cprose@cs.cmu.edu
www.lti.cs.cmu.edu/learn

Human language technologies have become an increasingly central component of computer science. Information retrieval, machine translation and speech technology are used daily by the general public, while text mining, natural language processing and language-based tutoring are common within more specialized professional or educational environments. The Language Technologies Institute prepares students for this world by offering a minor that gives you the opportunity to not only learn about language technologies, but to also apply that knowledge through a directed project.

Prerequisites
Prerequisites Units
15-122Principles of Imperative Computation12
15-150Principles of Functional Programming12
Recommended
21-241Matrices and Linear Transformations11
or 21-242 Matrix Theory
15-259Probability and Computing12
or 21-325 Probability
or 36-218 Probability Theory for Computer Scientists
Curriculum
Core requirement:
11-324Human Language for Artificial Intelligence12
Electives (choose 3):
11-344Machine Learning in Practice12
11-411Natural Language Processing12
11-441Machine Learning for Text and Graph-based Mining9
11-442Search Engines9
11-492Speech Processing12
11-711Advanced Natural Language Processing12
11-731Machine Translation and Sequence-to-Sequence Models12
11-737Multilingual Natural Language Processing.12
11-747Neural Networks for NLP12
11-751Speech Recognition and Understanding12
11-752Speech II: Phonetics, Prosody, Perception and Synthesis12
11-761Language and Statistics12
11-776Multimodal Affective Computing12
80-180Nature of Language9
80-280Linguistic Analysis9
Project:
A semester-long directed research project OR paper to provide hands-on experience and an in-depth study of a topic (in same area as a chosen elective)12
Double Counting of Courses

Students may double count 11-324 Human Language for Artificial Intelligence and 80-180 Nature of Language toward any other major or minor.

Machine Learning Minor

Dr. Matt Gormley, Program Director
Laura Winter, Program Coordinator
ml-minor@cs.cmu.edu
www.ml.cmu.edu/academics/minor-in-machine-learning.html

Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of this field.

Prerequisites

The 3 prerequisite courses must be taken before a student applies to the Machine Learning Minor.

Prerequisites Units
15-122Principles of Imperative Computation12
15-151Mathematical Foundations for Computer Science12
or 21-127 Concepts of Mathematics
or 21-128 Mathematical Concepts and Proofs
36-235Probability and Statistical Inference I9
or 36-218 Probability Theory for Computer Scientists
or 36-219 Probability Theory and Random Processes
or 36-225 Introduction to Probability Theory
or 15-259 Probability and Computing
or 21-325 Probability
Core Courses

The Machine Learning Minor has 2 core courses that provide a foundation in the field.

Core Courses
10-301Introduction to Machine Learning (Undergrad)12
or 10-315 Introduction to Machine Learning (SCS Majors)
10-403Deep Reinforcement Learning & Control12
or 10-405 Machine Learning with Large Datasets (Undergraduate)
or 10-417 Intermediate Deep Learning
or 10-418 Machine Learning for Structured Data
Electives

The Machine Learning Minor requires at least 3 electives of at least 9 units each in Machine Learning. Students may select one of the following options to satisfy the electives requirement:

  • 3 Principal courses
  • 2 Principal courses + 1 Interdisciplinary course
  • 2 Principal courses + 1 semester of CS Senior Honors Thesis or Senior Research
  • 1 Principal course + 2 semesters of CS Senior Honors Thesis or Senior Research

Students should note that some of these elective courses (those at the 600-level and higher) are primarily aimed at graduate students, and so should make sure that they are adequately prepared for them before enrolling.

Graduate-level cross-listings of these courses can also be used for the ML Minor, if the student is adequately prepared for the more advanced version and the home department approves the student's registration.

Principal Electives
10-403Deep Reinforcement Learning & Control12
or 10-703 Deep Reinforcement Learning & Control
10-405Machine Learning with Large Datasets (Undergraduate)12
or 10-605 Machine Learning with Large Datasets
or 10-745 Scalability in Machine Learning
10-414Deep Learning Systems: Algorithms and Implementation12
10-417Intermediate Deep Learning12
or 11-485 Introduction to Deep Learning
or 10-707 Advanced Deep Learning
10-418Machine Learning for Structured Data12
or 10-708 Probabilistic Graphical Models
10-425Introduction to Convex Optimization12
or 10-725 Convex Optimization
10-613Machine Learning Ethics and Society12
10-777Historical Advances in Machine Learning12
36-401Modern Regression9
Other courses as approved

Note: Courses must come from separate lines. For example, if 10-417 Intermediate Deep Learning is used for the ML Minor, 11-485 Introduction to Deep Learning cannot also be used for the ML Minor.

Interdisciplinary Electives
02-510Computational Genomics12
03-511Computational Molecular Biology and Genomics9
10-335Art and Machine Learning12
10-737Creative AIVar.
11-411Natural Language Processing12
11-441Machine Learning for Text and Graph-based Mining9
11-661Language and Statistics12
11-731Machine Translation and Sequence-to-Sequence Models12
11-751Speech Recognition and Understanding12
11-755Machine Learning for Signal Processing12
11-777Multimodal Machine Learning12
15-281Artificial Intelligence: Representation and Problem Solving12
15-386Neural Computation9
15-388Practical Data Science9
15-482Autonomous Agents12
16-311Introduction to Robotics12
16-385Computer Vision12
16-720Computer Vision12
16-745Optimal Control and Reinforcement Learning12
16-824Visual Learning and Recognition12
16-831Introduction to Robot Learning12
17-537Artificial Intelligence Methods for Social Good9
36-402Advanced Methods for Data Analysis9
36-462Special Topics: Methods of Statistical Learning9
36-463Special Topics: Multilevel and Hierarchical Models9
36-700Probability and Mathematical Statistics12
Other courses as approved

SCS Senior Honors Thesis

The SCS Senior Honors Thesis consists of 36 units of academic credit for this work. Up to 24 units (12 units each semester) may be counted towards the ML Minor. Students must consult with the Computer Science Department for information about the SCS Senior Honors Thesis. Once both student and advisor agree upon a project, the student should submit a one-page research proposal to the Machine Learning Concentration Director to confirm that the project will count for the Machine Learning Concentration.

07-599SCS Honors Undergraduate Research ThesisVar.
Senior Research

Senior research consists of 2 semesters of 10-500 Senior Research Project, totaling 24 units and counting as 2 electives.

The research must be a year-long senior project, supervised or co-supervised by a Machine Learning Core or Affiliated Faculty member. It is almost always conducted as two semester-long projects, and must be done in senior year. Some samples of available Machine Learning Senior Projects are available on the Machine Learning Department webpage.

Interested students should contact the faculty they wish to advise them to discuss the research project, before the semester in which research will take place. Once both student and advisor agree upon a project, the student should submit a one-page research proposal to the Machine Learning Minor Director to confirm that the project will count for the Machine Learning Minor.

The student should expect to meet with the Minor Director during both Senior Fall and Spring to discuss the project, and will present the work and submit a year-end write-up to the Minor Director at the end of Senior year.

10-500Senior Research Project24
Double Counting

No course in the Machine Learning Minor may be counted towards another SCS minor. Additionally, at least 3 courses (each being at least 9 units) must be used for only the Machine Learning Minor, not for any other major, minor, or concentration. (These double counting restrictions apply specifically to the Core Courses and the Electives. Prerequisites may be counted towards other majors, minors, and concentrations and do not count towards the 3 courses that must be used for only the Machine Learning Minor.)

GRADES

All courses for the Machine Learning Minor, including prerequisites, must be passed with a C or better.

ADMISSION

The Machine Learning Minor is open to undergraduate students in any major at Carnegie Mellon outside the School of Computer Science. (SCS students should instead consider the Machine Learning Concentration.) Students should apply for admission at least one semester before their expected graduation date, but are encouraged to apply as soon as they have taken the prerequisite classes for the minor. The application can be found on the Machine Learning Minor website.

Neural Computation Minor

Dr. Tai Sing Lee, Director
Melissa Stupka, Administrative Coordinator
https://www.cmu.edu/ni/academics/minor-in-neural-computation.html

Neural computation is a scientific enterprise to understand the neural basis of intelligent behaviors from a computational perspective. Study of neural computation includes, among others, decoding neural activities using statistical and machine learning techniques, and developing computational theories and neural models of perception, cognition, motor control, decision-making and learning. The neural computation minor allows students to learn about the brain from multiple perspectives, and to acquire the necessary background for graduate study in neural computation. Students enrolled in the minor will be exposed to, and hopefully participate in, the research effort in neural computation and computational neuroscience at Carnegie Mellon University.

The minor in Neural Computation is an intercollege minor jointly sponsored by the School of Computer Science, the Mellon College of Science, and the Dietrich College of Humanities and Social Sciences, and is coordinated by the Neuroscience Institute and the Center for the Neural Basis of Cognition (CNBC).

The Neural computation minor is open to students in any major of any college at Carnegie Mellon. It seeks to attract undergraduate students from computer science, psychology, engineering, biology, statistics, physics, and mathematics from SCS, CIT, H&SS and MCS.

The Neural Computation minor is open to students in any major of any college at Carnegie Mellon. It seeks to attract undergraduate students from computer science, psychology, engineering, biology, statistics, physics, and mathematics from SCS, CIT, Dietrich College and MCS. The primary objective of the minor is to encourage students in biology and psychology to take computer science, engineering and mathematics courses, to encourage students in computer science, engineering, statistics and physics to take courses in neuroscience and psychology, and to bring students from different disciplines together to form a community. The curriculum and course requirements are designed to maximize the participation of students from diverse academic disciplines. The program seeks to produce students with both basic computational skills and knowledge in cognitive science and neuroscience that are central to computational neuroscience.

APPLICATION

Students must apply for admission no later than November 30 of their senior years; an admission decision will usually be made within one month. Students are encouraged to apply as early as possible in their undergraduate careers so that the director of the Neural Computation minor can provide advice on their curriculum, but should contact the program director any time even after the deadline.

To apply, send email to the director of the Neural Computation minor Dr. Tai Sing Lee (tai@cnbc.cmu.edu) and copy Melissa Stupka (mstupka@andrew.cmu.edu). Include in your email:

  • Full name
  • Andrew ID
  • Preferred email address (if different)
  • Your class and College/School at Carnegie Mellon
  • Semester you intend to graduate
  • All (currently) declared majors and minors
  • Statement of purpose (maximum 1 page) – Describes why you want to take this minor and how it fits into your career goals
  • Proposed schedule of required courses for the Minor (this is your plan, NOT a commitment)
  • Research projects you might be interested in
Curriculum

The Minor in Neural Computation will require a total of five courses: four courses drawn from the four core areas (A: neural computation, B: neuroscience, C: cognitive psychology, D: intelligent system analysis), one from each area, and one additional depth elective chosen from one of the core areas that is outside the student’s major. The depth elective can be replaced by a one-year research project in computational neuroscience. No more than two courses can be double counted toward the student’s major or other minors. However, courses taken for general education requirements of the student’s degree are not considered to be double counted. A course taken to satisfy one core area cannot be used to satisfy the course requirement for another core area. The following listing presents a set of current possible courses in each area. Other computational neuroscience courses are being developed at Carnegie Mellon and University of Pittsburgh that will also satisfy core area A requirement and the requirements will be updated as they come on-line. Substitution is possible but requires approval.

A. Neural Computation
Units
15-386Neural Computation9
15-387Computational Perception9
15-883Computational Models of Neural Systems12
85-419Introduction to Parallel Distributed Processing9
86-375Computational Perception9
Pitt-Mathematics-1800 Introduction to Mathematical Neuroscience9
B. Neuroscience
03-362Cellular Neuroscience9
03-363Systems Neuroscience9
03-365Neural Correlates of Learning and Memory9
42-630Introduction to Neural Engineering
(crosslisted with 18-690)
12
85-765Cognitive Neuroscience9
Pitt-Neuroscience 1000 Introduction to Neuroscience9
C. Cognitive Psychology
85-211Cognitive Psychology9
85-213Human Information Processing and Artificial Intelligence9
85-412Cognitive Modeling9
85-419Introduction to Parallel Distributed Processing9
85-426Learning in Humans and Machines9
85-765Cognitive Neuroscience9
D. Intelligent System Analysis
10-301Introduction to Machine Learning (Undergrad)12
or 10-315 Introduction to Machine Learning (SCS Majors)
15-281Artificial Intelligence: Representation and Problem Solving12
15-386Neural Computation9
15-387Computational Perception9
15-494Cognitive Robotics: The Future of Robot Toys12
16-299Introduction to Feedback Control Systems12
16-311Introduction to Robotics12
16-385Computer Vision12
18-290Signals and Systems12
24-352Dynamic Systems and Controls12
36-225Introduction to Probability Theory9
36-247Statistics for Lab Sciences9
36-401Modern Regression9
36-410Introduction to Probability Modeling9
42-631Neural Data Analysis12
42-632Neural Signal Processing12
86-375Computational Perception9
86-631Neural Data Analysis12
Prerequisites

The required courses in the above four core areas require a number of basic prerequisites: basic programming skills at the level of 15-110 Principles of Computing and basic mathematical skills at the level of 21-122 Integration and Approximation or their equivalents. Some courses in Area D require additional prerequisites. Area B Biology courses require, at minimum, 03-121 Modern Biology. Students might skip the prerequisites if they have the permission of the instructor to take the required courses. Prerequisite courses are typically taken to satisfy the students' major or other requirements. In the event that these basic skill courses are not part of the prerequisite or required courses of a student's major, one of them can potentially count toward the five required courses (e.g. the depth elective), conditional on approval by the director of the minor program.

Research Requirements (Optional)

The minor itself does not require a research project. The student however may replace the depth elective with a year-long research project. In special circumstances, a research project can also be used to replace one of the five courses, as long as (1) the project is not required by the student's major or other minor, (2) the student has taken a course in each of the four core areas (not necessarily for the purpose of satisfying this minor's requirements), and (3) has taken at least three courses in this curriculum not counted toward the student's major or other minors. Students interested in participating in the research project should contact any faculty engaged in computational neuroscience or neural computation research at Carnegie Mellon or in the University of Pittsburgh. A useful webpage that provides listing of faculty in neural computation is https://www.cmu.edu/ni/academics/pnc/pnc-training-faculty.html. The director of the minor program will be happy to discuss with students about their research interest and direct them to the appropriate faculty.

Fellowship Opportunities

The Program in Neural Computation (PNC) administered by the Center for the Neural Basis of Cognition currently provides 3-4 competitive full-year fellowships ($11,000) to Carnegie Mellon undergraduate students to carry out mentored research in neural computation. The fellowship has course requirements similar to the requirements of the minor. Students do not apply to the fellowship program directly. They have to be nominated by the faculty members who are willing to mentor them. Therefore, students interested in the full-year fellowship program should contact and discuss research opportunities with any CNBC faculty at Carnegie Mellon or University of Pittsburgh working in the area of neural computation or computational neuroscience and ask for their nomination by sending email to Dr. Tai Sing Lee, who also administers the undergraduate fellowship program at Carnegie Mellon. See www.cnbc.cmu.edu/training/undergraduate/undergraduate-research-fellowships-in-computational-neuroscience/ for details.

The Program in Neural Computation also offers a summer training program for undergraduate students from any U.S. undergraduate college. The students will engage in a 10-week intense mentored research and attend a series of lectures in neural computation. See www.cnbc.cmu.edu/training/undergraduate/summer-undergraduate-research-program-in-computational-neuroscience/ for application information.

Software Engineering Minor

Michael Hilton, Director
mhilton@andrew.cmu.edu
http://isri.cmu.edu/education/undergrad

Effectively building modern software systems at scale requires not just programming skills, but also engineering skills. These skills include the ability to interact effectively with customers to gather the requirements for a system in a precise way; to develop a design that resolves competing quality attributes; to make tradeoffs among schedule, cost, features, and quality to maximize value to stakeholders; to work effectively with other engineers; and to assure the quality of the delivered software system. We hear regularly from industry that these skills are crucial to them, and that they are interested in students with a strong software engineering background.

The software engineering minor is designed to teach the fundamental tools, techniques, and processes of software engineering. Through internships and a mentored project experience, students gain an understanding of the issues of scale and complexity that motivate software engineering tools and techniques. The core curriculum includes material both on engineering the software product and on the process, teamwork, and management skills that are essential to successful engineering. Graduates of the program should have the technical, process, and teamwork skills to be immediately productive in a mature engineering organization.

Admission

The Software Engineering Minor is open to undergraduate students in any major in the university. We encourage students to submit applications no later than 3 days before the beginning of Spring and Fall course registration, so that subsequent decisions can help students plan their subsequent course schedule effectively.  However, students may petition the Director for admission outside this general schedule.

To apply, send the director an email.  Include in your email:

  • Full name
  • Andrew ID
  • Preferred email address (if different)
  • Semester you intend to graduate
  • QPA
  • All (currently) declared majors and minors, or home college if no major declared
  • Statement of purpose (maximum 1 page) - Describes why you want to take this minor and how it fits into your career goals
  • Proposed schedule of required courses and internship (this is your plan, NOT a commitment)
Prerequisite
17-214Principles of Software Construction: Objects, Design, and Concurrency12
Core Course Requirements
Complete both of the following courses.
17-313Foundations of Software Engineering12
17-413Software Engineering Practicum12
Electives

The minor requires three elective courses, one selected from each of the following categories:

1. One domain-independent course focused on technical software engineering material (min. 9 units): 
Must complete at least 9 units, may comprise one 9-12 unit course or multiple minis
15-316Software Foundations of Security and Privacy9
15-414Bug Catching: Automated Program Verification9
17-355Program Analysis12
17-356Software Engineering for Startups12
17-314Formal Methods
*Mini: pair with 17-324
6
17-324Advanced Formal Methods
*
6
17-612Business and Marketing Strategy
**Mini: pair with either 17-626 or 17-627
6
17-626Requirements for Information Systems
**
6
17-627Requirements for Embedded Systems
**
6
17-322Agile Methods
***Mini: pair with 17-332 and/or 17-442
6
17-332Software Project Management
***
6
17-442Software Management Theory
***
6
17-323Quality Assurance
****Mini: pair with 17-443
6
17-443Quality Management
****
6
17-335Software Architectures
Mini: add to other mini pairings as needed.
6
17-731Foundations of Privacy12
17-821Computer Simulation of Complex Socio-Technical Systems12
Crosslisted courses allowed. 
Other courses may be allowed, with prior approval from the Director of the Software Engineering Program.
2. One engineering-focused course with a significant software component (min. 9 units):
At least 9 units of the following:
15-410Operating System Design and Implementation15
15-412Operating System PracticumVar.
15-440Distributed Systems12
15-441Networking and the Internet12
15-445Database Systems12
17-437Web Application Development12
67-443Mobile Application Design and Development12
Crosslisted courses allowed 
Other courses may be allowed, with prior approval from the Director of the Software Engineering Program.
3. One course that explores computer science problems in society and industry, related to existing and emerging technologies and their associated social, political, legal, business, and organizational contexts (min. 9 units):
At least 9 units of the following:
17-200Ethics and Policy Issues in Computing9
17-331Information Security, Privacy, and Policy12
17-333Privacy Policy, Law, and Technology9
17-334Usable Privacy and Security9
17-562Law of Computer Technology9
19-403Policies of Wireless Systems12
70-311Organizational Behavior9
70-415Introduction to Entrepreneurship9
70-471Supply Chain Management9
Crosslisted courses allowed. 
Required Internship and Reflection Course

A software engineering internship of a minimum of 8 full-time weeks in an industrial setting is required. The student must be integrated into a team and exposed to industry pressures. The intern may work in development, management, quality assurance, or other relevant positions. The director of the SE minor program has sole discretion in approving an internship experience based on these criteria. Students should confirm that an internship position is appropriate before accepting it, but internships that fulfill the criteria will also be accepted after the fact.

  • 17-415 Software Engineering Reflection (required 6 unit course, number to be determined, to be offered Fall semester):  Each student will conduct an analysis of some personal software engineering experience, typically (but not always) based on the engineering internship above. The student will then write and edit a short paper presenting this analysis. Initial course meetings will cover the reflective, writing, and speaking process. In later meetings, each student will present his or her experience through a 30-45 minute talk, which will be evaluated for communication skills and critical reflective content. This course is limited to enrollment of 16, and students who are admitted to the minor program are given first priority.
Double Counting Rule

At most 2 of the courses used to fulfill the minor requirements may be counted towards any other major or minor program. This rule does not apply to 17-214 (a prerequisite for the minor) or courses counted for general education requirements, nor does it apply to double-counting with the SCS General Education requirements.

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