Updated July 17th, 2025
The information on this page is tentative and subject to change.
In the Artificial Intelligence (AI) major (CS29), students develop the knowledge and skills necessary to build, apply, and assess artificial intelligence technologies across disciplines and to ground AI studies in societal and professional contexts. Core topics include programming, data structures, algorithms, AI, machine learning (ML), and data ethics. Upper-division coursework includes core Artificial Intelligence classes, specialized electives, and application-focused courses from CSE and other departments (including Data Science, Cognitive Science, Mathematics, and Philosophy). AI electives explore computer vision, natural language processing, robotics, and others. Additional electives in specialization areas build depth and breadth within systems, theory/abstraction, and applications of computing.
CS29 Major Change Policy
For the 2025-2026 academic year, only students admitted directly into the CS29 Artificial Intelligence major by UCSD's Admissions Office will be permitted to major in CS29 Artificial Intelligence. The CSE Department will not accept internal major switches into the Artificial Intelligence major in the 2025-2026 academic year during this initial ramp-up phase.
CSE students majoring in CS25 Computer Engineering, CS26 Computer Science, and CS27 Computer Science with a Specialization in Bioinformatics may not request to switch to the CS29 Artificial Intelligence major during the 2025-2026 academic year. All requests to switch to CS29 in the Major/Minor tool will be disapproved, so please plan accordingly.
Students admitted into CS29 Artificial Intelligence may switch into another CSE major (CS25, CS26, or CS27) without restriction. However, they will not be able to switch back into CS29 until this policy is revisited.
This policy for internal major changes will be revisited in 2026, and more information about internal major changes between CSE’s four majors will be posted by Fall 2026.
Please note: CSE Advising cannot guarantee that CSE majors admitted before Fall 2025 will be permitted to major in CS29 Artificial Intelligence, as the current policy may remain in place.
CSE majors who are interested in studying Artificial Intelligence can use the “Focus Sheets” resource to select elective courses within the AI and machine learning subdisciplines.
Students who were not admitted to the CSE Department must apply to the CSE Department through the Selective Major Process. The AI major will not be an option for the Selective Major Process at this time. Visit the Continuing Students Selective Major website and the CSE Selective Major website for more information about this process. Many CSE classes are available to students in other majors. Visit the CSE Undergraduate information Homepage to see which classes are offered and for enrollment advice.
We appreciate your patience and understanding as we introduce this new major. Please reach out to CSE Advising in the Virtual Advising Center (vac.ucsd.edu) or during drop-in advising if you have questions.
Degree Planning:
- BS Artificial Intelligence Checklist: a checklist for all major requirements for students on the Fall 2025 curriculum
- CS29 Major Policies
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- All major requirements must be taken for a letter grade AND passed with a C- or better (with the exceptions of: CSE 91, CSE 95, CSE 197, CSE 198, and CSE 199).
- A maximum of 12 units of P/NP courses may count, chosen from: a maximum of 8 units of CSE 198 or CSE 199 or 199H; a maximum of 4 units of CSE 197.
- Students may use 8 units of CSE 198 or CSE 199 or CSE 199H towards CSE elective requirements.
- Students may use up to 8 units of ENG 100D/ENG 100L courses towards upper division CSE Elective credits (as part of the 8 units maximum of CSE 198/199/199H Special Studies courses allowed). You are NOT able to take ENG 100D twice.
- Students may use CSE 109 (2 units) towards upper division CSE Elective credits, as part of the 12 units maximum of P/NP courses allowed.
- A maximum of 12 units of CSE 190 can be used towards CSE elective credit. May be repeated for credit max 3 times (maximum of 12 units; assuming courses taken for a different topic).
- Please visit the CSE 190 website for current offerings and to view the tag for each course.
- Undergraduate students may use CSE graduate-level courses towards their major requirements, but may need a petition if they have taken the equivalent/similar undergraduate course. Undergraduate students must get instructor's permission and departmental approval (EASy request) to enroll in a graduate course. CSE 291's are topics courses and are counted as part of the maximum of three CSE 190's allowed for CSE electives.
- Untagged upper division CSE courses that may be used as CSE Electives are CSE 109 (2 units), CSE 190 (tagged based on offering), CSE 192, CSE 195, CSE 197, CSE 198, CSE 199, CSE 199H. CSE courses that may not be used as CSE Electives courses toward the AI degree are: CSE 180, CSE 180R.
2025-2026 CS29 Electives:
CS29 Artificial Intelligence majors must complete 12 units of AI Electives and 32 Units of Electives (8 units of Theory Electives, 8 units of Systems Electives, 8 units of Applications of Computing Electives, and 8 units of Open CSE Electives). Please see the "AI Electives" tab below for the list of AI Electives.
- AI Electives
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Courses that are pre-approved as both an AI Elective and a tagged Systems/Theory/Applications Elective can only be used to fulfill one major requirement. Each course can only fulfill one major requirement (either a core course, or an AI Elective, or a Systems Elective, or a Theory Elective, or an Applications Elective, or an Open CSE Elective). Please plan accordingly.
CSE 106 - Discrete and Continuous Optimization (4)
CSE 150A - Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision-Making (4)
CSE 150B - Introduction to Artificial Intelligence: Search and Reasoning (4)
CSE 152A - Introduction to Computer Vision I (4)
CSE 152B - Introduction to Computer Vision II (4)
CSE 153 or CSE 153R - Machine Learning for Music (4)
CSE 156 - Statistical Natural Language Processing (4)
CSE 158 or CSE 158R - Recommender Systems & Web Mining (4) or DSC 148 - Introduction to Data Mining (4) [Students may not receive credit for DSC 148 and CSE 158 or CSE 158R.]
COGS 108 - Data Science in Practice (4)
COGS 109 - Modeling and Data Analysis (4)
COGS 118A - Supervised Machine Learning Algorithms (4)
COGS 118B - Intro to Machine Learning II (4)
COGS 118C - Neural Signal Processing (4)
COGS 181 - Neural Networks/Deep Learning (4)
COGS 182 - Introduction to Reinforcement Learning (4)
COGS 185 - Advanced Machine Learning Methods (4)
COGS 186 - Genetic Algorithms (4)
COGS 188 - Artificial Intelligence Algorithms (4)
DSC 102 - Systems for Scalable Analytics (4)
DSC 120 - Signal Processing for Data Analysis (4)
DSC 140A - Probabilistic Modeling and Machine Learning (4)
DSC 140B - Representation Learning (4)
DSC 170 - Spatial Data Science and Applications (4)
ECE 172A - Introduction to Intelligent Systems: Robotics and Machine Intelligence (4)
ECE 175A - Elements of Machine Intelligence: Pattern Recognition and Machine Learning (4)
ECE 175B - Elements of Machine Intelligence: Probabilistic Reasoning and Graphical Models (4)
ECE 176 - Introduction to Deep Learning and Applications (4)
MATH 102 - Applied Linear Algebra (4)
MATH 170A - Introduction to Numerical Analysis: Linear Algebra (4)
MATH 173A - Optimization Methods for Data Science I (4)
MATH 173B - Optimization Methods for Data Science II (4)
MATH 181A - Introduction to Mathematical Statistics I (4)
MATH 181D - Statistical Learning (4)
MATH 182 - Hidden Data in Random Matrices (4) or DSC 155 - Hidden Data in Random Matrices (4) [Students will not receive credit for both MATH 182 and DSC 155.]
- Computer Science and Engineering
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Any upper-division CSE course between CSE 100-190, 193, 194 that is not being used for another major requirement (and is taken for a letter grade and passed with a C- or better) may be used towards an upper-division "CSE Elective" for the CS29 major.
Each CSE Elective course is “tagged” as Systems, Theory, Applications of Computing, and/or Open CSE Electives. Students may view the full list of tagged CSE courses on the CSE Undergraduate Program Catalog.
- Cognitive Science
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COGS 108 - Data Science in Practice (4) - Applications of Computing
COGS 109 - Modeling and Data Analysis (4) - Applications of Computing
COGS 118A - Supervised Machine Learning Algorithms (4) - Applications of Computing
COGS 118B - Intro to Machine Learning II (4) - Applications of Computing
COGS 118C - Neural Signal Processing (4) - Applications of Computing
COGS 120 - Interaction Design (5) - Applications of Computing
COGS 121 - Human Computer Interaction Programming Studio (4) - Applications of Computing
COGS 122 - Startup Studio (4) - Applications of Computing
COGS 123 - Social Computing (4) - Applications of Computing
COGS 124 - HCI Technical Systems Research (4) - Applications of Computing
COGS 125 - Advanced Interaction Design (4) - Applications of Computing
COGS 126 - Human-Computer Interaction (4) - Applications of Computing
COGS 127 - Designing Human-Data Interactions (4) - Applications of Computing
COGS 181 - Neural Networks/Deep Learning (4) - Applications of Computing
COGS 185 - Advanced Machine Learning Methods (4) - Applications of Computing
COGS 186 - Genetic Algorithms (4) - Applications of Computing
COGS 187A - Usability and Information Architecture (6) - Applications of Computing
COGS 187B - Practicum in Professional Web Design (4) - Applications of Computing
COGS 188 - Artificial Intelligence Algorithms (4) - Applications of Computing
COGS 189 - Brain Computer Interfaces (4) - Applications of Computing
Please use the UC San Diego EASy Course Pre-Authorization Forms for COGS course clearance.
- Data Science
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DSC 100 - Introduction to Data Management (4) - Applications of Computing
- Design
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DSGN 100 - Prototyping (4) - Applications of Computing
- Economics
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ECON 172A - Operations Research A (4) - Applications of Computing
ECON 172B - Operations Research B (4) - Applications of Computing
- Education Studies
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EDS 124AR - Teaching Computation in the Digital World (4) - Applications of Computing
EDS 124BR - Teaching Computational Thinking for Everyone (4) - Applications of Computing
- Electrical & Computer Engineering (ECE)
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ECE 111 - Advanced Digital Design Project (4) - Systems
ECE 140A - The Art of Product Engineering I (4) - Systems or Applications of Computing
ECE 140B - The Art of Product Engineering II (4) - Systems or Applications of Computing
ECE 148 - Introduction to Autonomous Vehicles (4) - Applications of Computing
- Engineering (Global Ties)
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ENG 100D/ENG 100L: Principles of Team Engineering: globalties.ucsd.edu
- Students may use up to 8 units of ENG 100D/ENG 100L courses towards upper division CSE Elective credits (as part of the 8 units maximum of CSE 198/199/199H Special Studies courses allowed). You are NOT able to take ENG 100D twice.
- Students must take ENG 100L twice (2 units each time) to receive credit for upper division CSE Elective (not exceeding the 8 units of CSE 198/199/199H Special Studies courses); students may not combine this course with another 2 unit or 6 unit course.
- Students may request to have their degree audit updated by contacting the Virtual Advising Center.
- Linguistics
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LIGN 165 - Computational Linguistics (4) - Applications of Computing
LIGN 167 - Deep Learning for Natural Language Understanding (4) - Applications of Computing
- Mathematics
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MATH 114 - Introduction to Computational Stochastics (4) - Applications of Computing
MATH 155A - Geometric Computer Graphics (4) - Applications of Computing
MATH 170A - Introduction to Numerical Analysis: Linear Algebra (4) - Theory
MATH 170B - Introduction to Numerical Analysis: Approximation and Nonlinear Equations (4) - Theory
MATH 170C - Introduction to Numerical Analysis: Ordinary Differential Equations (4) - Theory
MATH 171A - Introduction to Numerical Optimization: Linear Programming (4) - Theory
MATH 171B - Introduction to Numerical Optimization: Nonlinear Programming (4) - Theory
MATH 173A - Optimization Methods for Data Science I (4) - Theory
MATH 173B - Optimization Methods for Data Science II (4) - Theory
MATH 181D - Statistical Learning (4) - Theory
MATH 185 - Introduction to Computational Statistics (4) - Theory
MATH 187A - Introduction to Cryptography (4) - Theory
MATH 189 - Exploratory Data Analysis and Inference (4) - Applications of Computing
- Music
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MUS 171 - Computer Music I (4) - Applications of Computing
MUS 172 - Computer Music ll (4) - Applications of Computing
MUS 177 - Music Programming (4) - Applications of Computing
- Visual Arts
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VIS 141A - Computer Programming for the Arts I (4) - Applications of Computing
VIS 141B - Computer Programming for the Arts II (4) - Applications of Computing
CSE 25 and CSE 55 Course Descriptions
Two new core lower-division courses are being introduced for the AI major: CSE 25 Introduction to Artificial Intelligence and CSE 55 Foundations of Artificial Intelligence and Machine Learning.
CSE 25 provides a high-level introduction to AI using some programming to illustrate motivating examples and key ideas. CSE 55 focuses on the foundational mathematical and technological skills required for AI and ML and prepares students for upper division courses. Course descriptions are provided below.
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CSE 25: Introduction to Artificial Intelligence
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This course provides a first introduction to Artificial Intelligence (AI). It covers the definition of AI, the history of AI, the main approaches to AI, and example applications of AI and Machine Learning (ML). Concepts will be grounded in a range of real-world application projects in AI. Students will also be introduced to ethical issues around AI. Prerequisites: (COGS18 or CSE11 or CSE6R or CSE8A or CSE8B or DSC20). Restricted to students within the CS29 major. All other students will be allowed as space permits
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CSE 55: Foundations of Artificial Intelligence and Machine Learning
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This course prepares students with the mathematical foundation and programming skills required for more advanced Artificial Intelligence (AI) and Machine Learning (ML) courses. Topics include: applications of optimization and linear algebra in machine learning, including convex optimization, gradient-based methods, and representation learning. Theoretical concepts will be grounded in programming projects. Prerequisites: (CSE12) and (CSE25) and (CSE15L or CSE29) and (MATH18 or MATH31AH) and (MATH20C or MATH31BH). Restricted to students within the CS29 major. All other students will be allowed as space permits.
Academic Plans:
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Academic Planning Worksheet (link to copy a Google Sheet): blank worksheet for students to be able to create a sample long term plan which can be brought to an advising meeting
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Sample Plans By College: sample long term plan that includes college requirements
- Sample 4 Year Plan:
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Fall Quarter - Year One
Winter Quarter - Year One
Spring Quarter - Year One
CSE 8A*
CSE 11
CSE 20*
MATH 20A
CSE 25
CSE 12
Lower Division elective
MATH 20B
MATH 20C
Fall Quarter - Year Two
Winter Quarter - Year Two
Spring Quarter - Year Two
MATH 18
CSE 30
CSE 100
CSE 21*
CSE 55
CSE 101
CSE 29
CSE 103
General Science
Fall Quarter - Year Three
Winter Quarter - Year Three
Spring Quarter - Year Three
CSE 150A or CSE 150B
CSE 151A
AI 3
AI 1
AI 2
TH 1
Sys 1
PHIL 174
Fall Quarter - Year Four
Winter Quarter - Year Four
Spring Quarter - Year Four
App 1
App 2
Sys 2
Elective 1
Elective 2
Th 2
*1: Students who do not have programming experience should begin CSE 8A. Students who have programming experience may begin with CSE 11 (take CSE 12 and CSE 29 in the second quarter). Students who take CSE 8A should move on to CSE 11 and then continue in the sequence.
*2: CSE 20 may be substituted with MATH 109 or MATH 31CH. This is a manual update an advisor needs to make. Send a message through the Virtual Advising Center (VAC).
*3: CSE 21 may be substituted with MATH 154 or MATH 184. This is a manual update an advisor needs to make. Send a message through the Virtual Advising Center (VAC). *Effective Winter 2023: CSE 21 may be substituted with MATH 154 or MATH 184 or MATH 188*
*4: Open CSE Electives: CSE UD courses, including Special Studies along with any non-CSE courses that have any of the above tags. For a full list of policies and limitations on Open CSE Electives, please visit the CSE Electives website and our CSE course catalog.
- Sample Transfer Plan:
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Fall Quarter - Year One
Winter Quarter - Year One
Spring Quarter - Year One
CSE 21
CSE 30
CSE 101
CSE 25
CSE 55
CSE 103
CSE 29
CSE 100
Sys 1
App 1
Fall Quarter - Year Two
Winter Quarter - Year Two
Spring Quarter - Year Two
CSE 150A or CSE 150B
AI 2
AI 3
CSE 151A
Th 2
Sys 2
AI 1
App 2
Elective 1
Th 1
PHIL 174
Elective 2
- *4: Open CSE Electives: CSE UD courses, including Special Studies along with any non-CSE courses that have any of the above tags. For a full list of policies and limitations on Open CSE Electives, please visit the CSE Electives website and our CSE course catalog.