Program Overview

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Artificial Intelligence Program

The Artificial Intelligence Program at Carnegie Mellon University is designed to provide students with a comprehensive education in artificial intelligence, including machine learning, computer vision, natural language processing, and robotics. The program is interdisciplinary, with faculty members from the School of Computer Science, the Human-Computer Interaction Institute, the Software and Societal Systems Department, the Language Technologies Institute, the Machine Learning Department, and the Robotics Institute.

Overview

Carnegie Mellon University has led the world in artificial intelligence education and innovation since the field was created. The School of Computer Science offers 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.

Curriculum

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.

Math and Statistics

• 15-151: Mathematical Foundations for Computer Science
• 21-120: Differential and Integral Calculus
• 21-122: Integration and Approximation
• 21-241: Matrices and Linear Transformations
• 21-259: Calculus in Three Dimensions
• Probability and Statistics (one of the following options):
  • 36-218: Probability Theory for Computer Scientists
  • 15-259: Probability and Computing
  • 21-325 & 36-226: Probability and Introduction to Statistical Inference
  • 36-225 & 36-226: Introduction to Probability Theory and Introduction to Statistical Inference
  • 36-235 & 36-236: Probability and Statistical Inference I and Probability and Statistical Inference II
• Modern Regression:
  • 36-401: Modern Regression

Computer Science

• 15-122: Principles of Imperative Computation
• 15-150: Principles of Functional Programming
• 15-210: Parallel and Sequential Data Structures and Algorithms
• 15-213: Introduction to Computer Systems
• 15-251: Great Ideas in Theoretical Computer Science

Artificial Intelligence

• 07-280: Artificial Intelligence and Machine Learning I
• 07-380: Artificial Intelligence and Machine Learning II
• One of the following AI core courses:
  • 11-411: Natural Language Processing
  • 16-385: Computer Vision
• One Decision Making and Robotics course (min. 9 units):
  • 15-386: Neural Computation
  • 15-482: Autonomous Agents
  • 15-494: Cognitive Robotics: The Future of Robot Toys
  • 16-350: Planning Techniques for Robotics
  • 16-362: Mobile Robot Algorithms Laboratory
  • 16-384: Robot Kinematics and Dynamics
• One Machine Learning course from the following (min. 9 units):
  • 10-403: Deep Reinforcement Learning & Control
  • 10-405: Machine Learning with Large Datasets (Undergraduate)
  • 10-414: Deep Learning Systems: Algorithms and Implementation
  • 10-417: Intermediate Deep Learning
  • 10-418: Machine Learning for Structured Data
  • 10-422: Foundations of Learning, Game Theory, and Their Connections
  • 10-423: Generative AI
  • 10-424: Bayesian Methods in Machine Learning
  • 10-425: Introduction to Convex Optimization
  • 11-441: Machine Learning with Graphs
  • 11-485: Introduction to Deep Learning
  • 36-402: Advanced Methods for Data Analysis
• One Perception and Language course from the following (min. 9 units):
  • 11-442: Search Engines
  • 11-492: Speech Technology for Conversational AI
  • 15-387: Computational Perception
  • 15-463: Computational Photography
• One Human-AI Interaction course from the following (min. 12 units):
  • 05-317: Design of Artificial Intelligence Products
  • 05-318: Human AI Interaction
  • 05-391: Designing Human Centered Software
  • 16-467: Introduction to Human Robot Interaction

School of Computer Science Electives

• Two general computer science electives:
  • These electives can be from any SCS department (Computational Biology [02-], Human-Computer Interaction [05-], Interdisciplinary [07-], Machine Learning [10-], Language Technologies [11-], Computer Science [15-], Robotics [16-], or Software & Societal Systems [17-]). They must be 200-level or above and at least 9 units each.

Ethics Course

• One of the following courses:
  • 16-161: Artificial Intelligence and Humanity
  • 16-735: Ethics and Robotics
  • 17-200: Ethics and Policy Issues in Computing
  • 80-249: AI, Society, and Humanity

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 (MCS) and/or the College of Engineering (CIT).

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.

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 Artificial Intelligence in the spring of your first undergraduate year or transfer into the program no earlier than the spring of your sophomore. If you are already at CMU but not in SCS, you can apply to transfer into the program at a later date. Consult with the director or the program administrator of the BSAI program for information.

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.

Freshman Year

• Fall:
  • 07-128: First Year Immigration Course
  • 15-122: Principles of Imperative Computation
  • 15-151: Mathematical Foundations for Computer Science
  • 21-122: Integration and Approximation
  • 76-101: Interpretation and Argument
  • 99-101: Core@CMU
• Spring:
  • xx-180: Two Major Introduction Minis (02-180, 05-180, 07-180, 16-180)
  • 15-150: Principles of Functional Programming
  • 15-213: Introduction to Computer Systems
  • 21-241: Matrices and Linear Transformations

Sophomore Year

• Fall:
  • 07-280: Artificial Intelligence and Machine Learning I
  • 15-210: Parallel and Sequential Data Structures and Algorithms
  • 36-218: Probability Theory for Computer Scientists
  • xx-xxx: Science and Engineering Elective
  • xx-xxx: Ethics Elective
• Spring:
  • 07-380: Artificial Intelligence and Machine Learning II
  • 15-251: Great Ideas in Theoretical Computer Science
  • 21-259: Calculus in Three Dimensions
  • 85-xxx: Cognitive Studies Elective
  • xx-xxx: Humanities and Arts Elective

Junior Year

• Fall:
  • 11-411: Natural Language Processing
  • or 16-385: Computer Vision
  • 36-401: Modern Regression
  • xx-xxx: AI Elective: Machine Learning
  • xx-xxx: Humanities and Arts elective
  • xx-xxx: Free Elective
• Spring:
  • xx-xxx: AI Elective: Human-AI Interaction
  • xx-xxx: AI Elective: Decision Making and Robotics
  • xx-xxx: Science and Engineering elective
  • xx-xxx: Humanities and Arts elective
  • xx-xxx: Free Elective

Senior Year

• Fall:
  • xx-xxx: AI Elective: Perception and Language
  • xx-xxx: SCS Elective
  • xx-xxx: Science and Engineering Elective
  • xx-xxx: Humanities and Arts Elective
• Spring:
  • xx-xxx: SCS Elective
  • xx-xxx: Humanities and Arts Elective
  • xx-xxx: Free Elective
  • xx-xxx: Science and Engineering Elective

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 07-280. Students must earn a "B" average in all prerequisite coursework in order to be admitted to the additional major.

Prerequisites

• 15-112: Fundamentals of Programming and Computer Science

Math and Statistics Core

• 21-112: Integral Calculus
• or 21-120: Differential and Integral Calculus
• 21-127: Concepts of Mathematics
• or 21-128: Mathematical Concepts and Proofs
• or 15-151: Mathematical Foundations for Computer Science
• 21-122: Integration and Approximation
• 21-241: Matrices and Linear Transformations
• Probability and Statistics (one of):
  • 36-218: Probability Theory for Computer Scientists
  • 15-259: Probability and Computing
  • 21-325 & 36-226: Probability and Introduction to Statistical Inference
  • 36-225 & 36-226: Introduction to Probability Theory and Introduction to Statistical Inference
  • 36-235 & 36-236: Probability and Statistical Inference I and Probability and Statistical Inference II
• Modern Regression Course:
  • 36-401: Modern Regression

Computer Science Core

• 15-122: Principles of Imperative Computation
• 15-150: Principles of Functional Programming
• 15-210: Parallel and Sequential Data Structures and Algorithms
• 15-213: Introduction to Computer Systems
• 15-251: Great Ideas in Theoretical Computer Science

Artificial Intelligence Core

• 07-280: Artificial Intelligence and Machine Learning I
• 07-380: Artificial Intelligence and Machine Learning II

AI Cluster Electives

• Cognition and Action Cluster (1 course):
  • 15-386: Neural Computation
  • 15-482: Autonomous Agents
  • 15-494: Cognitive Robotics: The Future of Robot Toys
  • 16-350: Planning Techniques for Robotics
  • 16-362: Mobile Robot Algorithms Laboratory
  • 16-384: Robot Kinematics and Dynamics
• Machine Learning Cluster (1 course):
  • 10-403: Deep Reinforcement Learning & Control
  • 10-405: Machine Learning with Large Datasets (Undergraduate)
  • 10-414: Deep Learning Systems: Algorithms and Implementation
  • 10-417: Intermediate Deep Learning
  • 10-418: Machine Learning for Structured Data
  • 10-422: Foundations of Learning, Game Theory, and Their Connections
  • 10-423: Generative AI
  • 10-424: Bayesian Methods in Machine Learning
  • 10-425: Introduction to Convex Optimization
  • 11-441: Machine Learning with Graphs
  • 11-485: Introduction to Deep Learning
  • 36-402: Advanced Methods for Data Analysis
• Perception and Language Cluster (1 course):
  • 11-411: Natural Language Processing
  • 11-442: Search Engines
  • 11-492: Speech Technology for Conversational AI
  • 15-387: Computational Perception
  • 15-463: Computational Photography
  • 16-385: Computer Vision
• Human-AI Interaction Cluster (1 course):
  • 05-317: Design of Artificial Intelligence Products
  • 05-318: Human AI Interaction
  • 05-391: Designing Human Centered Software
  • 16-467: Introduction to Human Robot Interaction

Ethics and Human Cognition

• Ethics (1 course):
  • 16-161: Artificial Intelligence and Humanity
  • 16-735: Ethics and Robotics
  • 17-200: Ethics and Policy Issues in Computing
  • 80-249: AI, Society, and Humanity
• Human Cognition (1 course):
  • 85-110: Cognitive Psychology
  • 85-213: Human Information Processing and Artificial Intelligence
  • 85-413: Perception
  • 85-408: Visual Cognition
  • 85-421: Language and Thought

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 07-280. Students must earn a "C" average in all prerequisite coursework (including 07-280) in order to be admitted to the minor.

Prerequisites

• 15-122: Principles of Imperative Computation
• 21-112: Integral Calculus
• or 21-120: Differential and Integral Calculus
• or 21-259: Calculus in Three Dimensions
• 21-127: Concepts of Mathematics
• or 21-128: Mathematical Concepts and Proofs
• or 15-151: Mathematical Foundations for Computer Science
• 21-240: Matrix Algebra with Applications
• or 21-241: Matrices and Linear Transformations

Required Core

• 15-259: Probability and Computing
• or 21-325: Probability
• or 36-218: Probability Theory for Computer Scientists
• or 36-225: Introduction to Probability Theory
• or 36-235: Probability and Statistical Inference I
• 07-280: Artificial Intelligence and Machine Learning I
• 07-380: Artificial Intelligence and Machine Learning II

Technical Electives

• Cognition and Action Cluster:
  • 15-386: Neural Computation
  • 15-482: Autonomous Agents
  • 15-494: Cognitive Robotics: The Future of Robot Toys
  • 16-350: Planning Techniques for Robotics
  • 16-362: Mobile Robot Algorithms Laboratory
  • 16-384: Robot Kinematics and Dynamics
• Machine Learning Cluster:
  • 10-403: Deep Reinforcement Learning & Control
  • 10-405: Machine Learning with Large Datasets (Undergraduate)
  • 10-414: Deep Learning Systems: Algorithms and Implementation
  • 10-417: Intermediate Deep Learning
  • 10-418: Machine Learning for Structured Data
  • 10-422: Foundations of Learning, Game Theory, and Their Connections
  • 10-423: Generative AI
  • 10-424: Bayesian Methods in Machine Learning
  • 10-425: Introduction to Convex Optimization
  • 11-441: Machine Learning with Graphs
  • 11-485: Introduction to Deep Learning
• Perception and Language Cluster:
  • 11-411: Natural Language Processing
  • 11-442: Search Engines
  • 11-492: Speech Technology for Conversational AI
  • 15-387: Computational Perception
  • 15-463: Computational Photography
  • 16-385: Computer Vision

Societal Aspects of AI

• Human-AI Interaction Cluster:
  • 05-317: Design of Artificial Intelligence Products
  • 05-318: Human AI Interaction
  • 05-391: Designing Human Centered Software
  • 16-467: Introduction to Human Robot Interaction
• AI and Humanity Cluster:
  • 16-735: Ethics and Robotics
  • 17-200: Ethics and Policy Issues in Computing
  • 79-302: Killer Robots? The Ethics, Law, and Politics of Drones and A.I. in War
  • 80-249: AI, Society, and Humanity
  • 88-230: Human Intelligence and Human Stupidity
  • 88-275: Bubbles: Data Science for Human Minds
  • 90-442: Critical AI Studies for Public Policy
  • 94-441: Ethics and Politics of Data

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 pursuing a minor in AI can double count, at most, two courses total from the AI course requirements (not counting the prerequisite courses) 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.