This page contains an outline of the topics, content, and assignments for the semester.
Note that this schedule will be updated as the semester progresses, with all changes documented here.
Sept. 3 |
Defining AI |
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Sept. 8 |
Intro to machine learning (ML) |
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Sept. 10 |
Linear regression. |
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Sept. 15 |
Training |
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Sept. 17 |
Logistic regression |
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Sept. 22 |
Model evaluation |
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Sept. 24 |
Hyperparameter Tuning |
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Sept. 29 |
Bias-Variance Tradeoff |
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Sept. 29 |
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A1: Jupyter |
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Oct. 1 |
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Quiz |
Oct. 6 |
Machine Learning Engineering |
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Oct. 8 |
Introduction to Artificial Neural Networks |
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Oct. 13 |
Reading week - no lecture |
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Oct. 15 |
Reading week - no lecture |
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Oct. 20 |
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A2: ML |
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Oct. 20 |
Training Artificial Neural Networks |
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Oct. 22 |
Softmax, cross-entropy, regularization |
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Oct. 27 |
Convolutional Neural Networks |
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Oct. 29 |
Introduction to Search |
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Nov. 3 |
Informed Search |
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Nov. 5 |
Local Search |
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Nov. 10 |
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A3: DL |
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Nov. 10 |
Population-Based Metaheuristics |
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Nov. 12 |
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Quiz |
Nov. 17 |
Adversarial Search |
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Nov. 19 |
Monte Carlo Tree Search |
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Nov. 24 |
Formal Reasoning |
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Nov. 26 |
Neuro-symbolic |
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Dec. 1 |
Review (Tentative) |
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Dec. 1 |
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A4: Search |
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