Artificial Intelligence Program

Reid Simmons, Director of the BSAI program (NSH 3213)

Kaleigh Mitchell, Program Administrator (GHC 4113)
www.cs.cmu.edu/bs-in-artificial-intelligence

Overview

Carnegie Mellon University has led the world in artificial intelligence education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in Artificial Intelligence, which started in Fall 2018.

The BSAI program gives students the in-depth knowledge needed to transform large amounts of data into actionable decisions. The program and its curriculum focus on how complex inputs — such as vision, language and huge databases — can be used to make decisions or enhance human capabilities. The curriculum includes coursework in computer science, math, statistics, computational modeling, machine learning and symbolic computation. Because Carnegie Mellon is devoted to AI for social good, students will also take courses in ethics and social responsibility, with the option to participate in independent study projects that change the world for the better — in areas like healthcare, transportation and education.

Just as AI unites disciplines from machine learning to natural language processing, instruction in the BSAI program includes faculty members from the school's Computer Science Department, Human-Computer Interaction Institute, Institute for Software Research, Language Technologies Institute, Machine Learning Department and Robotics Institute.

Students in the BSAI program within the School of Computer Science are expected to acquire the following skills upon graduation:

  • Understand how to distill a real-world challenge as an artificial intelligence problem, involving explicit representation and learning of symbolic and numeric models; reasoning about such models; and using such models for decision making, action selection, and interaction with humans.
  • Design, analyze, implement, and use state-of-the-art AI and machine learning techniques for dealing with real-world data, including data involving vision, language, perception, and uncertainty.
  • Master the core concepts of computer science, with emphasis on data structures, programming, computing systems, and algorithm design, performance, and correctness across a variety of metrics (e.g., time, space, parallel vs. sequential implementation, what is computable).
  • Master the fundamentals of discrete mathematics, logic, theorem proving and explanation, probability and statistics, and optimization.
  • Describe, specify, and develop large-scale, open-ended artificial intelligence systems subject constraints such as performance, available data, and need for transparency. Communicate technical material effectively to technical and non-technical audiences.
  • Work productively both individually and in teams.
  • Recognize the social impact of artificial intelligence and the underlying responsibility to consider the ethical, privacy, moral, and legal implications of artificial intelligence technologies. 

Students who graduate with a bachelors degree in AI, will have the computer science savvy and skills our students are known for, with the added expertise in machine learning and automated reasoning that you'll need to build the AI of tomorrow.

How to Apply

If you're applying to CMU, you need to be accepted into the School of Computer Science. Once you're at CMU and enrolled in SCS, you can declare a BSAI major in the spring of your first year or transfer into the program in your sophomore or junior year. If you are already at CMU but not in SCS, you can apply to transfer into the program after your sophomore year. Consult with the director or the program administrator of the BSAI program for information.

Curriculum

BSAI majors will take core courses in math and statistics, computer science, artificial intelligence and ethics, along with general education courses in science and engineering, and humanities and arts. 

Math and Statistics

All of the following: Units
15-151Mathematical Foundations for Computer Science
(if not offered, substitute 21-127)
12
21-120Differential and Integral Calculus10
21-122Integration and Approximation10
21-241Matrices and Linear Transformations11
21-259Calculus in Three Dimensions
or 21-266, or 21-268, or 21-269
10
36-218Probability Theory for Computer Scientists
or: (15-259 or 21-325 or 36-225) and 36-226
9
36-401Modern Regression9

Computer Science

All of the following: Units
15-122Principles of Imperative Computation
(students without credit or a waiver for 15-112, Fundamentals of Programming and Computer Science, must take 15-112 before 15-122)
12
15-150Principles of Functional Programming12
15-210Parallel and Sequential Data Structures and Algorithms12
15-213Introduction to Computer Systems12
15-251Great Ideas in Theoretical Computer Science12

Artificial Intelligence

All of the following AI core courses: Units
07-180Concepts in Artificial Intelligence5
15-281Artificial Intelligence: Representation and Problem Solving12
10-315Introduction to Machine Learning (SCS Majors)12
plus one of the following AI core courses:
16-385Computer Vision12
11-411Natural Language Processing12
One Decision Making and Robotics course (min. 9 units): Units
15-386Neural Computation9
15-482Autonomous Agents12
15-494Cognitive Robotics: The Future of Robot Toys12
16-350Planning Techniques for Robotics12
16-362Mobile Robot Algorithms Laboratory12
16-384Robot Kinematics and Dynamics12
others as designated by the AI Undergraduate Program
One Machine Learning course from the following (min.9 units):
10-403Deep Reinforcement Learning & Control12
10-405Machine Learning with Large Datasets (Undergraduate)12
10-414Deep Learning Systems: Algorithms and Implementation12
10-417Intermediate Deep Learning12
10-418Machine Learning for Structured Data12
10-422Foundations of Learning, Game Theory, and Their Connections12
11-441Machine Learning for Text and Graph-based Mining9
11-485Introduction to Deep Learning9
36-402Advanced Methods for Data Analysis9
others as designated by the AI Undergraduate Program
One Perception and Language course from the following (min. 9 units):
11-442Search Engines9
11-492Speech Processing12
15-387Computational Perception9
15-463Computational Photography12
16-421Vision Sensors12
others as designated by the AI Undergraduate Program
One Human-AI Interaction course from the following (min. 12 units):
05-317Design of Artificial Intelligence Products12
05-318Human AI Interaction12
05-391Designing Human Centered Software12
16-467Human Robot Interaction12
others as designated by the AI Undergraduate Program

School of Computer Science Electives

Two general computer science electives: Units
These electives can be from any SCS department; 200-level or above, at least 9 units each (see exceptions below): Computer Science [15-], Computational Biology [02-], Human Computer Interaction [05-], Machine Learning [10-], Language Technologies [11-], Robotics [16-], and Software Engineering [17-]. (NOTE: The following undergraduate courses do NOT count as Computer Science electives: 02-201, 02-223, 02-250, 02-261, 11-423, 15-351, 16-223, 17-200, 17-333, 17-562. Some IDEATE courses and some SCS undergraduate and graduate courses might not be allowed based on course content. Consult with a CS undergraduate advisor before registration to determine eligibility for this requirement.)18

Ethics Course

One of the following courses: Units
80-249AI, Society, and Humanity9
17-200Ethics and Policy Issues in Computing9
16-735Ethics and Robotics12

SCIENCE AND ENGINEERING

All candidates for the bachelor's degree in Artificial Intelligence must complete a minimum of 36 units offered by the Mellon College of Science and/or the College of Engineering (CIT). These courses offer students an opportunity to explore scientific and engineering domains that can influence their effectiveness as computer scientists upon graduation.

Requirements for this component of the degree are listed under the SCS main page under General Education Requirements.

Humanities and Arts

All candidates for the bachelor's degree in Artificial Intelligence must complete a minimum of 63 units offered by the College of Humanities & Social Sciences and/or the College of Fine Arts. These courses offer students breadth in their education and perspectives and provide students with a better appreciation of social, artistic, cultural, political and economic issues that can influence their effectiveness as computer scientists upon graduation.

Requirements for this component of the degree are listed under the SCS main page under General Education RequirementsSPECIAL NOTE FOR AI STUDENTS: AI majors must satisfy Category 1 of the General Education requirements by taking one of the following Cognitive Studies (Category 1A) courses:  

  • 85-211 Cognitive Psychology
  • 85-213 Human Information Processing and Artificial Intelligence
  • 85-370 Perception
  • 85-408 Visual Cognition
  • 85-421 Language and Thought

SCS First year seminar

The following course is designed to acquaint incoming students with computer science at CMU:

07-128First Year Immigration Course3

Computing @ Carnegie Mellon

The following course is required of all students to familiarize them with the campus computing environment:

99-101Computing @ Carnegie Mellon3

Free Electives

A free elective is any Carnegie Mellon course. However, a maximum of nine (9) units of Physical Education and/or Military Science (ROTC) and/or Student-Led (StuCo) courses may be used toward fulfilling graduation requirements.

Summary of Degree Requirements
AreaCoursesUnits
Mathematics771
Computer Science560
Artificial Intelligence880
SCS Electives218
Ethics19
Science/Engineering436
Humanities/Arts (includes Cognitive Studies)763
SCS First Year Seminar13
Computing @ Carnegie Mellon13
Free Electivesvaries17
360

Undergraduate Research Thesis

AI majors may use the SCS Honors Research Thesis as part of their degree. The SCS Honors Undergraduate Research Thesis (07-599) typically starts in the fall semester of the senior year, and spans the entire senior year. Students receive a total of 36 units of academic credit for the thesis work, 18 units per semester. Up to 18 units can be counted toward SCS elective requirements (9 per semester for 2 semesters maximum).  Students interested in research may also consider using 07-300 Research and Innovation in Computer Science in their junior year since this course will introduce students to various research projects going on in the School of Computer Science that may lead to a senior thesis. This course leads to a subsequent practicum that allows students to complete a small-scale research study or experiment and present a research poster. Students who use the practicum to start their senior thesis can use these units toward the required 36 units.

For more information about the SCS Honors Research Thesis, refer to the SCS Honors Research Thesis section for learning objectives, application requirements and expected outcomes.

BSAI Roadmap: Sample Course Sequence

The sample given below is for a student who already has credit for introductory programming and introductory calculus. Students with no credit for introductory programming will take 15-112 in their first semester and shift some CS courses to later semesters after consulting with their academic advisor; students with no credit for calculus will take 21-120 in their first semester and shift 21-122 and 21-259 to subsequent semesters. These students should still be able to complete their degree in four years given the light load of their senior year. Students with credit for 21-120 and 21-122 may start with a more advanced math class (e.g. 21-241) in their first year. It is recommended that students keep their academic load lighter for their senior fall semester to account for offsite job interviews or for their senior spring semester to account for visits to graduate schools.

FRESHMAN YEAR:

Fall Units
07-128First Year Immigration Course3
15-122Principles of Imperative Computation12
15-151Mathematical Foundations for Computer Science12
21-122Integration and Approximation10
76-101Interpretation and Argument9
99-101Computing @ Carnegie Mellon3
 49
Spring Units
07-180Concepts in Artificial Intelligence5
15-150Principles of Functional Programming12
15-213Introduction to Computer Systems12
21-241Matrices and Linear Transformations11
21-259Calculus in Three Dimensions10
 50

SOPHOMORE YEAR:

Fall Units
15-210Parallel and Sequential Data Structures and Algorithms12
15-281Artificial Intelligence: Representation and Problem Solving12
36-218Probability Theory for Computer Scientists9
xx-xxxScience and Engineering Elective9
xx-xxxEthics Elective9
 51
Spring Units
10-315Introduction to Machine Learning (SCS Majors)12
15-251Great Ideas in Theoretical Computer Science12
85-xxxCognitive Studies Elective9
xx-xxxScience and Engineering Elective9
xx-xxxHumanities and Arts Elective9
 51

JUNIOR YEAR:

Fall Units
11-411Natural Language Processing12
or 16-385 Computer Vision
36-401Modern Regression9
xx-xxxAI Elective: Machine Learning9
xx-xxxHumanities and Arts elective9
xx-xxxFree Elective9
 48
Spring Units
xx-xxxAI Elective: Human-AI Interaction12
xx-xxxAI Elective: Decision Making and Robotics9
xx-xxxScience and Engineering elective9
xx-xxxHumanities and Arts elective9
xx-xxxFree Elective9
 48

 SENIOR YEAR:

Fall Units
xx-xxxAI Elective: Perception and Language9
xx-xxxSCS Elective9
xx-xxxScience and Engineering Elective9
xx-xxxHumanities and Arts Elective9
 36
Spring Units
xx-xxxSCS Elective9
xx-xxxHumanities and Arts Elective9
xx-xxxFree Elective9
 27

Minimum number of units required for the degree:360

The flexibility in the curriculum allows many different schedules, of which the above is only one possibility. Some elective courses are offered only once per year (Fall or Spring). AI cluster electives (decision making and robotics, machine learning, perception and language, and human-AI interaction) may be taken in any order and in any semester if prerequisites are met and seats are available. Constrained electives are shown in the specific semesters in the schedule above as an example only. Students should consult with their academic advisor to determine the best elective options depending on course availability, their academic interests and their career goals.   

Additional Major in Artificial Intelligence

Students interested in pursuing an additional major in Artificial Intelligence should first consult with the Program Administrator. Students must have all prerequisites completed, 21-112 or 21,120, 15-122, 15-150, one of 15-210, 15-213, or 15-251, as well as 15-281 or 10-315. Students must earn a "B" average in all prerequisite coursework in order to be admitted to the additional major. The additional major requires 6 mathematics courses, 5 computer science courses, 2 artificial intelligence courses, 4 courses from AI cluster areas, 1 course in ethics, and 1 course in human cognition.

Prerequisites

(1 course) Units
15-112Fundamentals of Programming and Computer Science12

The following courses are required for the Addition Major in Artificial Intelligence:

Math and Statistics Core 

(6 courses) Units
21-112Calculus II10
or 21-120 Differential and Integral Calculus
21-127Concepts of Mathematics12
or 21-128 Mathematical Concepts and Proofs
or 15-151 Mathematical Foundations for Computer Science
21-122Integration and Approximation10
21-241Matrices and Linear Transformations11
Probability and Statistics (one of)
36-225-36-226Introduction to Probability Theory - Introduction to Statistical Inference18
36-218Probability Theory for Computer Scientists9
15-259-15-260Probability and Computing - Statistics and Computing15
21-325-36-226Probability - Introduction to Statistical Inference18
Modern Regression Course
36-401Modern Regression9

Computer Science Core

(5 courses) Units
15-122Principles of Imperative Computation12
15-150Principles of Functional Programming12
15-210Parallel and Sequential Data Structures and Algorithms12
15-213Introduction to Computer Systems12
15-251Great Ideas in Theoretical Computer Science12

Artificial Intelligence Core

(2 courses) Units
15-281Artificial Intelligence: Representation and Problem Solving12
10-315Introduction to Machine Learning (SCS Majors)12

AI Cluster Electives

(4 courses, one from each cluster area) Units
Cognition and Action Cluster (1 course)
15-386Neural Computation9
15-482Autonomous Agents12
15-494Cognitive Robotics: The Future of Robot Toys12
16-350Planning Techniques for Robotics12
16-362Mobile Robot Algorithms Laboratory12
16-384Robot Kinematics and Dynamics12
Machine Learning Cluster (1 course)
10-403Deep Reinforcement Learning & Control12
10-405Machine Learning with Large Datasets (Undergraduate)12
10-414Deep Learning Systems: Algorithms and Implementation12
10-417Intermediate Deep Learning12
10-418Machine Learning for Structured Data12
10-422Foundations of Learning, Game Theory, and Their Connections12
11-441Machine Learning for Text and Graph-based Mining9
11-485Introduction to Deep Learning9
36-402Advanced Methods for Data Analysis9
Perception and Language Cluster (1 course)
11-411Natural Language Processing12
11-442Search Engines9
11-492Speech Processing12
15-387Computational Perception9
15-463Computational Photography12
16-385Computer Vision12
Human-AI Interaction Cluster (1 course)
05-317Design of Artificial Intelligence Products12
05-318Human AI Interaction12
05-391Designing Human Centered Software12
16-467Human Robot Interaction12

Ethics and Human Cognition

(2 courses, one from each cluster area)
Ethics (1 course)
16-735Ethics and Robotics12
17-200Ethics and Policy Issues in Computing9
80-249AI, Society, and Humanity9
Human Cognition (1 course)
85-211Cognitive Psychology9
85-213Human Information Processing and Artificial Intelligence9
85-370Perception9
85-345Meaning in Mind and Brain9
85-408Visual Cognition9
85-435Biologically Intelligent Exploration9

*Note that Concepts in Artificial Intelligence (07-180) is not required for additional majors, although students interested in the additional major in AI are encouraged to take 07-180 prior to taking 15-281 or 10-315.

Double Counting Restrictions

Students pursuing an additional major in AI can double count at most five courses total, from the Computer Science Core, the Artificial Intelligence Core, and the AI Cluster Electives, towards all other majors and minors they're pursuing. The Mathematics, Ethics, and Human Cognition courses may double count without restriction, except for 36-402 (Advanced Methods for Data Analysis), which is part of the Machine Learning Cluster. Students with majors that overlap substantially with AI should consult with the Program Administrator to review their audit for any potential issues.

Artificial Intelligence Minor

Students interested in pursuing a minor in Artificial Intelligence should first consult with the Program Administrator after completion of the prerequisites and 15-281 or 10-301/10-315. Students must earn a "C" average in all prerequisite coursework (including 15-281 or 10-301/10-315) in order to be admitted to the minor. The minor includes 3 required core courses, and 5 courses from AI cluster areas.

Prerequisites

(4 courses) Units
15-122Principles of Imperative Computation12
21-112Calculus II10
or 21-120 Differential and Integral Calculus
or 21-259 Calculus in Three Dimensions
21-127Concepts of Mathematics12
or 21-128 Mathematical Concepts and Proofs
or 15-151 Mathematical Foundations for Computer Science
21-240Matrix Algebra with Applications10
or 21-241 Matrices and Linear Transformations

The following courses are required for the Minor in Artificial Intelligence: 

Required Core

(3 courses) Units
*Two mini courses can be combined to form one 9 unit course.
36-225Introduction to Probability Theory9
or 21-325 Probability
or 36-218 Probability Theory for Computer Scientists
or 15-259 Probability and Computing
15-281Artificial Intelligence: Representation and Problem Solving12
10-301Introduction to Machine Learning (Undergrad)12
or 10-315 Introduction to Machine Learning (SCS Majors)

Technical Electives 

(2 courses from any of the three areas) Units
Cognition and Action Cluster
15-386Neural Computation9
15-482Autonomous Agents12
15-494Cognitive Robotics: The Future of Robot Toys12
16-350Planning Techniques for Robotics12
16-362Mobile Robot Algorithms Laboratory12
16-384Robot Kinematics and Dynamics12
85-213Human Information Processing and Artificial Intelligence9
85-412Cognitive Modeling9
85-419Introduction to Parallel Distributed Processing9
85-435Biologically Intelligent Exploration9
Machine Learning Cluster
10-403Deep Reinforcement Learning & Control12
10-405Machine Learning with Large Datasets (Undergraduate)12
10-414Deep Learning Systems: Algorithms and Implementation12
10-417Intermediate Deep Learning12
10-418Machine Learning for Structured Data12
10-422Foundations of Learning, Game Theory, and Their Connections12
11-441Machine Learning for Text and Graph-based Mining9
11-485Introduction to Deep Learning9
15-388Practical Data Science9
or 67-364 Practical Data Science
36-401Modern Regression9
36-402Advanced Methods for Data Analysis9
Perception and Language Cluster
11-411Natural Language Processing12
11-442Search Engines9
11-492Speech Processing12
15-387Computational Perception9
15-463Computational Photography12
16-385Computer Vision12
85-370Perception9
85-345Meaning in Mind and Brain9
85-408Visual Cognition9

Societal Aspects of AI 

(1 course from one of the two cluster areas) Units
*Two mini courses can be combined to form one 9 unit course.
Human-AI Interaction Cluster
05-317Design of Artificial Intelligence Products12
05-318Human AI Interaction12
05-391Designing Human Centered Software12
16-467Human Robot Interaction12
AI and Humanity Cluster
16-735Ethics and Robotics12
17-200Ethics and Policy Issues in Computing9
79-302Killer Robots:The Ethics, Law, and Politics of Lethal Autonomous Weapons Systems9
80-249AI, Society, and Humanity9
88-230Human Intelligence and Human Stupidity9
88-275Bubbles: Data Science for Human Minds9
88-380Dynamic Decisions9
90-442Critical AI Studies for Public Policy6
94-441Ethics and Politics of Data6

Double Counting Restriction

Students pursuing a minor in AI can double count, at most, two courses total from the AI course requirements towards all other majors and minors they're pursuing. Students with majors that overlap substantially with AI should consult with the Program Administrator to review their audit for any potential issues.

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