CS 161 - Fundamentals of Artificial Intelligence - Fall 2026
- Instructor: Professor Guy Van den Broeck
- Lecture: Fall 2026, Tuesday and Thursday, 2:00 to 3:50 pm; Moore 100
- Final exam: Monday, December 7, 3:00 to 6:00 pm; room TBD
- Course staff and contact information: See BruinLearn for TA names, instructor and TA email addresses, discussion schedules, and office hours.
Course Description
This course studies the design of intelligent agents. It introduces the fundamental problem-solving and knowledge-representation paradigms of artificial intelligence. We will study state-space and problem reduction methods, brute-force and heuristic search, planning techniques, two-player games, and recent developments in game AI. In knowledge representation and reasoning, we will cover propositional and first-order logic and their inference algorithms. Finally, the course covers probabilistic approaches to AI, such as Bayesian networks, and machine learning algorithms to improve the agent's performance with experience.
Prerequisites
This course requires knowledge of basic computer science, algorithms, complexity analysis, and programming principles.
Class Attendance
Attendance at ordinary lectures is optional. The quiz and midterm take place in class, and the final takes place during the scheduled final exam period. Participation in the assigned homework discussions is required for discussion credit. If you miss a lecture, you are responsible for its material and announcements. Some lecture material is not covered in the textbook.
Schedule
The quiz is on Thursday, October 22, and the midterm is on Thursday, November 12. The quiz will take 45 to 60 minutes. The midterm uses the full class period: 1 hour 50 minutes, from 2:00 to 3:50 pm. A study guide will be released before each assessment (quiz, midterm, and final) detailing its scope. The final covers the entire course.
| Week | Date | Location | Topic | Reading |
|---|---|---|---|---|
| Week 0 | Moore 100 | Course introduction: What is AI? | Chapter 1, 2 | |
| Week 1 | Instructor sick, no class | |||
| Moore 100 | Uninformed search strategies | Chapter 3 | ||
| Week 2 | Moore 100 | Informed search strategies | Chapter 3 | |
| Moore 100 | Heuristics | Chapter 3 | ||
| Week 3 | Moore 100 | Constraint satisfaction | Chapter 6 | |
| Moore 100 | Constraint satisfaction and local search | Chapter 6, 4 | ||
| Week 4 | Moore 100 | Game playing | Chapter 5 | |
| Moore 100 | Cumulative quiz (45 to 60 min) | |||
| Week 5 | Moore 100 | Propositional logic: representation | Chapter 7 | |
| Moore 100 | Propositional logic: inference | Chapter 7 | ||
| Week 6 | Moore 100 | Propositional logic: inference | Chapter 7 | |
| Moore 100 | Reasoning under uncertainty | Chapter 12 | ||
| Week 7 | Moore 100 | Bayesian networks: representation | Chapter 13 | |
| Moore 100 | Cumulative midterm (full 1 hour 50 minutes) | |||
| Week 8 | Moore 100 | Bayesian networks: inference | Chapter 13 | |
| Moore 100 | Machine learning basics | Chapter 19 | ||
| Week 9 | Moore 100 | Machine learning basics | Chapter 19 | |
| Thanksgiving, no class | ||||
| Week 10 | Moore 100 | First-order logic | Chapter 8, 9 | |
| Moore 100 | First-order logic | Chapter 8, 9 | ||
| Finals | TBD | Final exam, 3:00 PM to 6:00 PM (room TBD) |
Grading
| Component | Weight |
|---|---|
| Individual homework submissions (completion) | 10% |
| Homework peer discussions (completion) | 6% |
| Quiz | 14% |
| Midterm | 28% |
| Final | 42% |
| Total | 100% |
Both homework components are graded for completion. The quiz, midterm, and final are cumulative: the quiz covers approximately the first third of the course, the midterm covers approximately the first two-thirds, and the final covers the entire course. Their weights are in a 1:2:3 ratio. The quiz, midterm, and final are closed book, with no notes. The quiz and midterm contain a mix of free-form and multiple-choice questions. The final is entirely multiple choice. A simple calculator is allowed during the quiz, midterm, and final.
Homework and Peer Discussion
Work through each assignment individually and submit within one week. AI agents are allowed for homework. You must understand your submission and be able to explain your reasoning.
After submitting, discuss your solutions and questions in your randomly assigned group of three from your discussion section. Time is reserved during the Friday discussion after each deadline; alternatively, arrange a Zoom call or an in-person meeting with your assigned group.
Completion credit requires genuine work, not perfect answers. Discussion credit requires active participation: explain your reasoning and difficulties, compare approaches, and work through disagreements. Blank or token submissions and attendance without participation do not earn credit. See the honor code.
Late homework submissions: 25% of the assignment's total possible points will be deducted for each day the submission is late.
Textbook
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. (4th Edition), Pearson 2020.
Available from:
- UCLA Bookstore
- Online Stores
Additional Readings
- David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents
Honor Code
You may use AI agents for homework. Your submission must reflect your own engagement with the assignment. The peer-discussion phase is a required part of the homework process. In your assigned group, actively explain your reasoning, listen to your peers, and discuss the approaches or difficulties that arise. You may compare solutions during this phase, but you may not copy another student's write-up or submit a joint solution.
Claiming individual homework completion means that you personally worked through the assignment and that your submission honestly represents the work you did. Report incomplete work and difficulties honestly. Completion grading does not require every answer to be correct, but it does require genuine effort; you must understand and be able to explain the work you submit, including AI-assisted parts.
Claiming peer-discussion completion means that you actually took part in a meaningful discussion with your assigned peers. Being listed as a group member or being present without contributing is not sufficient. Identify the peers with whom you actually discussed the work. Do not claim a discussion that did not happen or certify participation that did not occur.
You may always ask the instructor or TAs for help. Credit ideas, code, text, and other material that came from someone else, including contributions from your discussion partners. AI agents may provide partial solutions, provided you acknowledge their contribution and work through the reasoning until you understand and can explain it. You may not use old solution sets or post them publicly.
The quiz, midterm, and final are closed book, with no notes. You are responsible for honoring these restrictions throughout each assessment. Consulting books or notes during the quiz or exams violates this honor code.
Copying another student's work, misrepresenting completion, or making a false participation claim violates this honor code.