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 |
Learning Algorithms |
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| Sept. 15 |
Linear regression and gradient descent |
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| Sept. 17 |
Logistic regression |
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| Sept. 22 |
Cross-entropy, geometric interpretation |
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| Sept. 24 |
Performance evaluation |
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| Sept. 29 |
Model Evaluation and Hyperparameter Tuning |
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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 |
Training Artificial Neural Networks (Part 1) |
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| Oct. 22 |
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A2: ML |
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| Oct. 22 |
Training Artificial Neural Networks (Part 2) |
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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 |
Population-Based Metaheuristics |
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| Nov. 10 |
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A3: DL |
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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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