Updated June 29th, 2026
The information on this page is tentative and subject to change.
In the Artificial Intelligence (AI) major, 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.
B.S. Artificial Intelligence Major Change Policy
For the 2026-2027 academic year, only students admitted directly into the Artificial Intelligence major by UCSD's Admissions Office will be permitted to major in Artificial Intelligence. The CSE Department will not accept internal major switches into the Artificial Intelligence major in the 2026-2027 academic year during this initial ramp-up phase.
CSE students majoring in Computer Engineering, Computer Science, and Computer Science with a Specialization in Bioinformatics may not request to switch to the Artificial Intelligence major during the 2026-2027 academic year. All requests to switch to B.S. Artificial Intelligence in the Triton Student System (TSS) will be disapproved, so please plan accordingly.
Students admitted into Artificial Intelligence may switch into another CSE major without restriction. However, they will not be able to switch back into B.S. Artificial Intelligence until this policy is revisited.
This policy for internal major changes will be revisited in 2027, and more information about internal major changes between CSE’s four majors will be posted by Fall 2027.
Please note: CSE Advising cannot guarantee that CSE majors admitted before Fall 2027 will be permitted to major in 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/booking 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 2026 curriculum
- B.S. Artificial Intelligence 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-089, CSE-091, CSE-095, CSE 109, 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 (TEA request) to enroll/book 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.
2026-2027 B.S. Artificial Intelligence Electives:
Artificial Intelligence majors: please see your degree checklist for your admit year to find more information about the upper division elective requirements.
- 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 -20 - Signal Processing for Data Analysis (4)
DSC-140A - Probabilistic Modeling and Machine Learning (4)
DSC-140B - Representation Learning (4)
DSC-148 - Introduction to Data Mining (4) or CSE-158 or CSE-158R - Recommender Systems & Web Mining (4) [Students may not receive credit for DSC-148 and CSE-158 or CSE-158R.]
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
-
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 B.S. Artificial Intelligence 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 Triton Enrollment Authorization (TEA) available via the Triton Student System (TSS) for COGS course clearance.
- Data Science
-
DSC-100 - Introduction to Data Management (4) - Applications of Computing
DSC-102 - Systems for Scalable Analytics (4) - Systems
DSC-120 - Signal Processing for Data Analysis (4) - Systems
DSC-123A- Optimization Methods for Data Science I (4) - Theory
DSC-123B- Optimization Methods for Data Science II (4) - Theory
- Design
-
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 (4) /ENG-100L (2) - Applications of Computing
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 may request to have their degree audit updated by contacting the Virtual Advising Center.
- Linguistics
-
LIGN-165 - Computational Linguistics (4) - Applications of Computing
LIGN-167 - Deep Learning for Natural Language Understanding (4) - Applications of Computing
- Mathematics
-
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
B.S. Artificial Intelligence Checklist (FA25 admits only)
- BS Artificial Intelligence Checklist: a checklist for all major requirements for students on the Fall 2025 curriculum
- Policies
-
- All major requirements must be taken for a letter grade AND passed with a C- or better (with the exceptions of: CSE-089, CSE-091, CSE-095, CSE-109, 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 (TEA request) to enroll/book 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.
CSE-025 and CSE-055 Course Descriptions
Two new core lower-division courses are being introduced for the AI major: CSE-025 Introduction to Artificial Intelligence and CSE-055 Foundations of Artificial Intelligence and Machine Learning.
CSE-025 provides a high-level introduction to AI using some programming to illustrate motivating examples and key ideas. CSE-055 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-025: 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: (COGS-018 or CSE-011 or CSE-006R or CSE-008A or CSE-008B or DSC-020). Restricted to students within the B.S. Artificial Intelligence major. All other students will be allowed as space permits
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CSE-055: 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: (CSE-012) and (CSE-025) and (CSE-015L or CSE-029) and (MATH-018 or MATH-031AH) and (MATH-020C or MATH-031BH). Restricted to students within the B.S. Artificial Intelligence major. All other students will be allowed as space permits.
Academic Plans:
-
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
-
Sample Plans By College: sample long term plan that includes college requirements
- Sample 4 Year Plan:
-
Year
Fall
Winter
Spring
First Year
CSE-008A*
MATH-020A
Lower Division Elective
CSE-011
CSE-025
MATH-020B
CSE-020*
CSE-012
MATH-020C
Second Year MATH-018
CSE-021
CSE-029
CSE-030
CSE-055
Statistics
CSE-100
CSE-101
General Science
Third Year CSE-150A or 150B
AI 1
Sys 1
CSE-151A
AI 2
Ethics
CSE-151B
AI 3
TH 1
Fourth Year App 1
Sys 2
App 2
TH 2
Elective 1
- Sample Transfer Plan:
-
Year
Fall
Winter
Spring
First Year CSE-021
CSE-025
CSE-029
CSE-030
CSE-055
CSE-100
CSE-101
Sys 1
App 1
Statistics
Second Year CSE-150A or 150B
CSE-151A
AI 1
TH 1
CSE-151B
AI 2
TH 2
Ethics
App 2
AI 3
Sys 2
Elective 1
- Sample 4 Year Plan (FA25 admits ONLY):
-
Year
Fall
Winter
Spring
First Year
CSE-008A*
MATH-020A
Lower Division Elective
CSE-011
CSE-025
MATH-020B
CSE-020*
CSE-012
MATH-020C
Second Year MATH-018
CSE-021
CSE-029
CSE-030
CSE-055
Statistics
CSE-100
CSE-101
General Science
Third Year CSE-150A or 150B or 151B
AI 1
Sys 1
CSE-151A
AI 2
Ethics
AI 3
TH 1
Fourth Year App 1
Elective 1
App 2
Elective 2
Sys 2
TH 2
*1: Students who do not have programming experience should begin CSE-08A. Students who have programming experience may begin with CSE-011 (take CSE-012 and CSE-029 in the second quarter). Students who take CSE-08A should move on to CSE-011 and then continue in the sequence.
*2: CSE-020 may be substituted with MATH-109 or MATH-031CH. This is a manual update an advisor needs to make. Send a message through the Virtual Advising Center (VAC).
*3: CSE-021 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 (FA25 admits ONLY):
-
Year
Fall
Winter
Spring
First Year CSE-021
CSE-025
CSE-029
CSE-030
CSE-055
CSE-100
CSE-101
Sys 1
App 1
Statistics
Second Year CSE-150A or 150B
CSE-151A
AI 1
TH 1
AI 2
TH 2
App 2
Ethics
AI 3
Sys 2
Elective 1
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.