Bachelor of Science in Artificial Intelligence
From foundations to intelligent systems.
The B.S. in Artificial Intelligence is structured as a progression: Students build a strong technical base, develop expertise in modern AI methods, and apply their knowledge to real-world problems.
Strong Foundations
Computing, algorithms, probability, and optimization.
Core AI Methods
Machine learning, deep learning, natural language processing, and computer vision.
Responsible AI
Fairness, bias, privacy, and societal impact as core principles.
Capstone Experience
Build systems that work in practice, not just in theory.
Core Areas
Build the mathematical and computational foundation for modern AI.
Foundations
Computing principles:
- CMPSC 41: Discrete Mathematics for Computer Science
- CMPSC 42: Logic and Automata
- CMPSC 130A: Data Structures & Algorithms
- CMPSC 130B: Algorithm Design & Analysis
Probability, statistics, and optimization:
- CMPSC 25: Data Programming and Analytics for AI (new)
- ECE 139: Probability and Statistics
- CMPSC 135: Mathematics of Artificial Intelligence (new)
Core AI
Learn how systems learn from data and interact with the world.
- CMPSC 165A: Artificial Intelligence
- CMPSC 165B: Machine Learning
- CMPSC 187: Deep Learning
- CMPSC 188: Natural Language Processing
- CMPSC 181: Computer Vision
Responsible AI
Understand the broader impact of intelligent systems.
- CMPSC 1A: AI Seminar (new)
- CMPSC 183: Ethics, Privacy, Fairness, and Bias in AI (new)
Capstone Experience
Apply your knowledge to a real-world project that integrates modeling, systems, and evaluation.
Degree Requirements
Students complete 180 total units, consistent with College of Engineering guidelines.
General Education
8 GE courses (outside the major). University requirements:
- Ethnicity
- American History & Institutions
- Writing
Preparation for the Major
| Course Code | Name | Units |
|---|---|---|
| Math 3A | Calculus with Applications I | 4 |
| Math 3B | Calculus with Applications II | 4 |
| Math 4A | Linear Algebra with Applications | 4 |
| Math 4B | Differential Equations | 4 |
| Math 6A | Vector Calculus | 4 |
| CMPSC 1A | AI Seminar New Course | 3 |
| CMPSC 9 | Intermediate Python Programming | 4 |
| CMPSC 25 | Data Programming and Analytics for Artificial Intelligence New Course | 4 |
| CMPSC 41 | Discrete Mathematics for Computer Science | 4 |
| CMPSC 42 | Logic and Automata | 4 |
| ECE 139 | Probability and Statistics | 4 |
Sample 4-Year Plan
| Fall | Winter | Spring | |
|---|---|---|---|
| Year 1 | CMPSC 81 (4) MATH 4A (4) G.E. (4) G.E. (4) |
CMPSC 9 (4) MATH 3B (4) Science Elective (4) G.E. (4) |
CMPSC 25 (4) MATH 4A (4) Science Elective (4) G.E. (4) |
| Year 2 | MATH 4B (4) G.E. (4) Free Elective (4) Science Elective (4) |
MATH 6A (4) ECE 139 (4) CMPSC 1A (3) Science Elective (4) |
CMPSC 41 (4) G.E. (4) Free Elective (4) Science Elective (4) |
| Year 3 | CMPSC 42 (4) CMPSC 130A (4) CMPSC 135 (4) G.E. (4) |
CMPSC 130B (4) CMPSC 165A (4) CMPSC 165B (4) G.E. (4) |
CMPSC/ECE 181 (4) CMPSC 187 (4) CMPSC 188 (4) CMPSC 183 (4) |
| Year 4 | Elective (4) Elective (4) Elective (4) |
Elective (4) Elective (4) Elective (4) |
Elective (4) Elective (4) Elective (4) |
1Consult Computer Science academic advisor for placement information.
Unit breakdown:
- Prep for major required courses: (as listed)
- GE requirements: 32
- Upper division required courses: 36
- Major electives: 36
- Science electives: 20
- Free electives:
- Total: 180