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| September | ||||
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| Monday | Tuesday | Wednesday | Thursday | Friday |
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15:30-16:20 Lecture
CSE2 G01 Background: Data Structures, Algorithms, Math, Python, and Coding with AI slides |
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| October | ||||
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| Monday | Tuesday | Wednesday | Thursday | Friday |
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23:59 A0 (Python Warm-up; participation item) due
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15:30-16:20 Lecture
CSE2 G01 SSS II: Formulation, Basic Algorithms
16:30-17:00 Small-group meetings with the instructor (15-min sessions; sign up on Ed)
CSE 624 |
14:00-15:00 Small-group meetings with the instructor (15-min sessions; sign up on Ed)
CSE 624
15:30-16:20 Lecture
CSE2 G01 SSS III: Searching Weighted Graphs and the A* Algorithm |
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15:30-16:20 Lecture
CSE2 G01 Comparing and Designing Heuristics
23:59 A1 (Formulation and Basic Search) due
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15:30-16:20 Lecture
CSE2 G01 Adversarial Search I: Static Evaluation, Minimax Search
16:30-17:00 Small-group meetings with the instructor (15-min sessions; sign up on Ed)
CSE 624 |
15:30-16:20 Lecture
CSE2 G01 Adversarial Search II: Alpha-Beta Pruning |
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15:30-16:20 Lecture
CSE2 G01 Adversarial Search III: Zobrist Hashing
23:59 A2 (Heuristic Search) due
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15:30-16:20 Lecture
CSE2 G01 Expectimax Search and Probability |
15:30-16:20 Lecture
CSE2 G01 Markov Decision Processes (e.g., for Robot World Modeling) |
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15:30-16:20 Lecture
CSE2 G01 MDP Values |
15:30-16:20 Lecture
CSE2 G01 Bellman Equations and Value Iteration |
15:30-16:20 Lecture
CSE2 G01 Value Iteration (continued)
23:59 A3 (Adversarial Search: K-in-a-Row) due
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| November | ||||
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| Monday | Tuesday | Wednesday | Thursday | Friday |
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15:30-16:20 Lecture
CSE2 G01 Q-Learning |
15:30-16:20 Lecture
CSE2 G01 Q-Learning (continued) ; Introducing a Markov Decision Process for Towers of Hanoi |
15:30-16:20 Lecture
CSE2 G01 Joint Probability Distributions
23:59 A4 (Written Exercises I) due
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15:30-16:20 Lecture
CSE2 G01 Bayes Nets; Counting Free Parameters |
Veteran's Day
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15:30-16:20 Lecture
CSE2 G01 Bayes Net Inference and Refactoring |
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09:00-17:00 Midterm exam window, tentative (Allen School Testing Center; reserve a seat)
Allen School Testing Center
15:30-16:20 Lecture
CSE2 G01 D-Separation (Reasoning about Factored Prob. Distributions) |
09:00-17:00-(tentative) Midterm exam window, tentative (Allen School Testing Center; reserve a seat)
Allen School Testing Center |
09:00-17:00-(tent.) Midterm exam window, tentative (Allen School Testing Center; reserve a seat)
Allen School Testing Center
15:30-16:20 Lecture
CSE2 G01 Markov Models (a Special Factoring Structure for BNs) |
09:00-17:00-(tent.) Midterm exam window, tentative (Allen School Testing Center; reserve a seat)
Allen School Testing Center |
15:30-16:20 Lecture
CSE2 G01 Hidden Markov Models and the Viterbi Algorithm |
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15:30-16:20 Lecture
CSE2 G01 Ethics I: The Asimov Laws, Trolley Problems
23:59 A5 (Reinforcement Learning) due
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15:30-16:20 Lecture
CSE2 G01 Perceptrons (incl. Multiclass Perceptrons) |
Thanksgiving
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Native American Heritage Day
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15:30-16:20 Lecture
CSE2 G01 Math Background for Deep Learning: Activation Functions, Loss, Gradients
23:59 A6 (Written Exercises II) due
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15:30-16:20 Lecture
CSE2 G01 Deep Learning I: Multilayer Neural Nets, Backpropagation, and Training |
15:30-16:20 Lecture
CSE2 G01 Deep Learning II: Language Models, Embeddings, Self-Attention |
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| December | ||||
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| Monday | Tuesday | Wednesday | Thursday | Friday |
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15:30-16:20 Lecture
CSE2 G01 Transformers in More Depth; Training and Using LLMs
23:59 A7 (Deep Learning) due
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15:30-16:20 Lecture
CSE2 G01 Tournament results, and course review |
15:30-16:20 Lecture
CSE2 G01 The Future of AI: Opportunities and Challenges; Course Wrap-up and Comments on the Final Exam |
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14:30-16:20 Final exam
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