CSE 415 Winter 2025
Course Calendar

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January
MondayTuesdayWednesdayThursdayFriday
06
13:30-14:20 Lecture
SIG 134
Introduction
slides
07 08
13:30-14:20 Lecture
SIG 134
Python Overview
slides
09 10
13:30-14:20 Lecture
SIG 134
State-Space Search I: States, Operators, Moves, Spaces
slides
13
13:30-14:20 Lecture
SIG 134
SSS II: Formulation, Basic Algorithms
slides
14 15
13:30-14:20 Lecture
SIG 134
SSS III: Searching Weighted Graphs
slides
16 17
13:30-14:20 Lecture
SIG 134
A* Algorithm Details
slides
23:59 A1 (Python Warm-up) due
20
MLK Day
21 22
13:30-14:20 Lecture
SIG 134
Comparing and Designing Heuristics
slides
23:59 A2 Part A (Blind Search) due
23 24
13:30-14:20 Lecture
SIG 134
Adversarial Search I: Static Evaluation, Minimax Search
slides
27
13:30-14:20 Lecture
SIG 134
Adversarial Search II: Alpha-Beta Pruning
slides
23:59 A2 Parts B and C (Heuristic Search) due
28 29
13:30-14:20 Lecture
SIG 134
Adversarial Search III: Zobrist Hashing
slides
30 31
13:30-14:20 Lecture
SIG 134
Expectimax Search and Probability
slides
February
MondayTuesdayWednesdayThursdayFriday
03
13:30-14:20 Lecture
SIG 134
Markov Decision Processes (e.g., for Robot World Modeling)
slides
23:59 A3 (Exercises I) due
04 05
13:30-14:20 Lecture
SIG 134
MDP Values
slides
06 07
13:30-14:20 Midterm exam
10
13:30-14:20 Lecture
SIG 134
Bellman Equations and Value Iteration
slides
11 12
13:30-14:20 Lecture
SIG 134
Value Iteration (continued)
13 14
13:30-14:20 Lecture
SIG 134
Q-Learning
Q-L slides Mini-Crawler slides
17
President's Day
18 19
13:30-14:20 Lecture
SIG 134
Joint Probability Distributions
slides
20 21
13:30-14:20 Lecture
SIG 134
Bayes Nets
(slides continue from Wed.)
23:59 A4 (Game-Playing Agents) due
24
13:30-14:20 Lecture
SIG 134
D-Separation (Reasoning about Factored Prob. Distributions)
slides
25 26
13:30-14:20 Lecture
SIG 134
Markov Models (a Special Factoring Structure for BNs)
slides
27 28
13:30-14:20 Lecture
SIG 134
Hidden Markov Models and the Viterbi Algorithm
slides
23:59 A5 (Implementing Reinf. Learning) due
March
MondayTuesdayWednesdayThursdayFriday
03
13:30-14:20 Lecture
SIG 134
Perceptrons
slides
04 05
13:30-14:20 Lecture
SIG 134
Multiclass Perceptrons; Ethics I: The Asimov Laws, Trolley Problems
slides
06 07
13:30-14:20 Lecture
SIG 134
Math Background for Deep Learning and Large Language Models
slides
10
13:30-14:20 Lecture
SIG 134
NN Architectures incl. Transformers
slides
23:59 A6 (Exercises II) due
11 12
13:30-14:20 Lecture
SIG 134
The Future of AI: Opportunities and Challenges
slides
13 14
13:30-14:20 Lecture
SIG 134
Course Wrap-up and Comments on the Final Exam
slides
17
14:30-16:20 Final exam
18 19 20 21