Week 1 Oct 4 |
Introduction; Supervised Learning Setup; kNNs |
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Week 2 Oct 11 |
No lecture |
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Week 3 Oct 18 |
Linear models for regression |
Project 1 |
Week 4 Oct 25 |
Linear Models for Classification; The Perceptron; Gradient Descent |
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Week 5 Nov 8 |
Linear Classification |
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Week 6 Nov 15 |
Lab 1 |
Project 1 due |
Week 7 Nov 22 |
Lab 2 - Lecture: LDA & Logistic Regression |
Project 2 |
Week 9 Nov 29 |
Support Vector Machines and Kernels |
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Week 10 Dec 6 |
Bias Variance Decomposition; Regularization |
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Week 11 Dec 13 |
Lab 3 - Lecture: Validation recap and Kernels |
Project 3 |
Week 12 Dec 20 |
Trees |
Project 2 due |
Week 13 Jan 10 |
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Week 14 Jan 17 |
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Week 15 Jan 23 |
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Week 15 Jan 24 |
Lab 4 |
Project 3 due |