Artificial Intelligence, B.S.
Bachelor of Science in Artificial Intelligence
Overview
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.
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 | |
| 15-151 | Mathematical Foundations for Computer Science (if not offered, substitute 21-127) | 12 |
| 21-120 | Differential and Integral Calculus | 10 |
| 21-122 | Integration and Approximation | 10 |
| 21-241 | Matrices and Linear Transformations | 11 |
| 21-259 | Calculus in Three Dimensions or 21-266, or 21-268, or 21-269 | 10 |
| Probability and Statistics (one of the following options): | ||
| 36-218 | Probability Theory for Computer Scientists | 9 |
| 15-259 | Probability and Computing | 12 |
| 21-325 & 36-226 | Probability and Introduction to Statistical Inference | 18 |
| 21-425 & 36-226 | Probability and Martingales and Introduction to Statistical Inference | 18 |
| 36-225 & 36-226 | Introduction to Probability Theory and Introduction to Statistical Inference | 18 |
| 36-235 & 36-236 | Probability and Statistical Inference I and Probability and Statistical Inference II | 18 |
| plus Modern Regression: | ||
| 36-401 | Modern Regression | 9 |
Computer Science
| All of the following: | Units | |
| 15-122 | Principles 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 |
| 15-150 | Principles of Functional Programming | 12 |
| 15-210 | Parallel and Sequential Data Structures and Algorithms | 12 |
| 15-213 | Introduction to Computer Systems | 12 |
| 15-251 | Great Ideas in Theoretical Computer Science | 12 |
Artificial Intelligence
| All of the following AI core courses: | Units | |
| 07-280 | Artificial Intelligence and Machine Learning I | 12 |
| 07-380 | Artificial Intelligence and Machine Learning II | 12 |
| plus one of the following AI core courses: | ||
| 11-411 | Natural Language Processing | 12 |
| 16-385 | Computer Vision | 12 |
| One Decision Making and Robotics course (min. 9 units): | Units | |
| 15-386 | Neural Computation | 9 |
| 15-482 | Autonomous Agents | 12 |
| 15-494 | Cognitive Robotics: The Future of Robot Toys | 12 |
| 16-350 | Planning Techniques for Robotics | 12 |
| 16-362 | Mobile Robot Algorithms Laboratory | 12 |
| 16-384 | Robot Kinematics and Dynamics | 12 |
| others as designated by the AI Undergraduate Program | ||
| One Machine Learning course from the following (min. 9 units): | ||
| 10-403 | Deep Reinforcement Learning & Control | 12 |
| 10-405 | Machine Learning with Large Datasets (Undergraduate) | 12 |
| 10-414 | Deep Learning Systems: Algorithms and Implementation | 12 |
| 10-417 | Intermediate Deep Learning | 12 |
| 10-418 | Machine Learning for Structured Data | 12 |
| 10-422 | Foundations of Learning, Game Theory, and Their Connections | 12 |
| 10-423 | Generative AI | 12 |
| 10-424 | Bayesian Methods in Machine Learning | 12 |
| 10-425 | Introduction to Convex Optimization | 12 |
| 11-441 | Machine Learning with Graphs | 9 |
| 11-485 | Introduction to Deep Learning | 12 |
| 36-402 | Advanced Methods for Data Analysis | 9 |
| others as designated by the AI Undergraduate Program | ||
| One Perception and Language course from the following (min. 9 units): | ||
| 11-442 | Search Engines | 9 |
| 11-492 | Speech Technology for Conversational AI | 12 |
| 15-387 | Computational Perception | 9 |
| 15-463 | Computational Photography | 12 |
| others as designated by the AI Undergraduate Program | ||
| One Human-AI Interaction course from the following (min. 12 units): | ||
| 05-317 | Design of Artificial Intelligence Products | 12 |
| 05-318 | Human AI Interaction | 12 |
| 05-391 | Designing Human Centered Software | 12 |
| 16-467 | Introduction to Human Robot Interaction | 12 |
| 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 (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, with the following exceptions: | 18 | |
| Students who take two of the major-intro mini-courses (02-180, 05-180, 07-180, or 16-180) during their first year may combine these two mini-courses together to count as one SCS elective; | ||
| The following courses do NOT count as SCS electives: 02-201, 02-223, 02-250, 02-261, 05-200, 07-402, 11-423, 15-351, 16-211, 16-223, 16-224, 16-397, 16-480, 17-200, 17-333, 17-562; Some IDEATE courses and some SCS undergraduate and graduate courses might not be allowed based on course content. Always consult with an AI undergraduate advisor before registration to determine eligibility for this requirement. | ||
Ethics Course
| One of the following courses: | Units | |
| 16-161 | Artificial Intelligence and Humanity | 12 |
| 16-735 | Ethics and Robotics | 12 |
| 17-200 | Ethics and Policy Issues in Computing | 9 |
| 80-249 | AI, Society, and Humanity | 9 |
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). 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 Requirements. SPECIAL 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:
| 85-110 | Cognitive Psychology | 9 |
| 85-213 | Human Information Processing and Artificial Intelligence | 9 |
| 85-408 | Visual Cognition | 9 |
| 85-421 | Language and Thought | 9 |
| 85-413 | Perception | 9 |
SCS First year seminar
The following course is designed to acquaint incoming students with computer science at CMU:
| 07-128 | First Year Seminar | 3 |
CoRE@CMU
The following course is required of all CMU students:
| 99-101 | Core@CMU | 3 |
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
| Area | Courses | Units |
| Mathematics | 7 | 71 |
| Computer Science | 5 | 60 |
| Artificial Intelligence | 7 | 75 |
| SCS Electives | 2 | 18 |
| Ethics | 1 | 9 |
| Science/Engineering | 4 | 36 |
| Humanities/Arts (includes Cognitive Studies) | 7 | 63 |
| SCS First Year Seminar | 1 | 3 |
| Core@CMU | 1 | 3 |
| Free Electives | varies | 22 |
| 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 | |
| 07-128 | First Year Seminar | 3 |
| 15-122 | Principles of Imperative Computation | 12 |
| 15-151 | Mathematical Foundations for Computer Science | 12 |
| 21-122 | Integration and Approximation | 10 |
| 76-101 | Interpretation and Argument | 9 |
| 99-101 | Core@CMU | 3 |
| 49 | ||
| Spring | Units | |
| xx-180 | Two Major Introduction Minis (02-180, 05-180, 07-180, 16-180) | 10 |
| 15-150 | Principles of Functional Programming | 12 |
| 15-213 | Introduction to Computer Systems | 12 |
| 21-241 | Matrices and Linear Transformations | 11 |
| 45 | ||
SOPHOMORE YEAR:
| Fall | Units | |
| 07-280 | Artificial Intelligence and Machine Learning I | 12 |
| 15-210 | Parallel and Sequential Data Structures and Algorithms | 12 |
| 36-218 | Probability Theory for Computer Scientists | 9 |
| xx-xxx | Science and Engineering Elective | 9 |
| xx-xxx | Ethics Elective | 9 |
| 51 | ||
| Spring | Units | |
| 07-380 | Artificial Intelligence and Machine Learning II | 12 |
| 15-251 | Great Ideas in Theoretical Computer Science | 12 |
| 21-259 | Calculus in Three Dimensions | 10 |
| 85-xxx | Cognitive Studies Elective | 9 |
| xx-xxx | Humanities and Arts Elective | 9 |
| 52 | ||
JUNIOR YEAR:
| Fall | Units | |
| 11-411 | Natural Language Processing | 12 |
| or 16-385 | Computer Vision | |
| 36-401 | Modern Regression | 9 |
| xx-xxx | AI Elective: Machine Learning | 9 |
| xx-xxx | Humanities and Arts elective | 9 |
| xx-xxx | Free Elective | 9 |
| 48 | ||
| Spring | Units | |
| xx-xxx | AI Elective: Human-AI Interaction | 12 |
| xx-xxx | AI Elective: Decision Making and Robotics | 9 |
| xx-xxx | Science and Engineering elective | 9 |
| xx-xxx | Humanities and Arts elective | 9 |
| xx-xxx | Free Elective | 9 |
| 48 | ||
SENIOR YEAR:
| Fall | Units | |
| xx-xxx | AI Elective: Perception and Language | 9 |
| xx-xxx | SCS Elective | 9 |
| xx-xxx | Science and Engineering Elective | 9 |
| xx-xxx | Humanities and Arts Elective | 9 |
| 36 | ||
| Spring | Units | |
| xx-xxx | SCS Elective | 9 |
| xx-xxx | Humanities and Arts Elective | 9 |
| xx-xxx | Free Elective | 9 |
| xx-xxx | Science and Engineering Elective | 9 |
| 36 | ||
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.