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schedule
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Schedule
Follow this link to view all lecture videos.
Announcements
- Feb 22: A3 Neural Network Regression, now includes link to `A3grader.tar` that contains `A3grader.py`.
January
| Week | Topic | Material | Reading | Assignments |
|---|---|---|---|---|
| Week 1: Jan 19 - Jan 22 | Overview. Intro to machine learning. Python. | 01 Course Overview, 02 Matrices and Plotting, | Text: Sections 1.1-1.5. Section 1 of Scipy Lecture Notes | |
| Week 2: Jan 25 - Jan 29 | Probability distributions and regression. | 03 Linear Regression, 04 Gaussian Distributions, 05 Fitting Gaussians, 06 Probabilistic Linear Regression | Sections 4.1-4.2, 4.6-4.9, 5.8-5.9 | A1 Linear Regression due Friday, January 29th at 10:00 PM. Download and unzip A1 Grader.zip Here are five examples of good solutions: A1a, A1b, A1c, A1d, A1e |
February
| Week | Topic | Material | Reading | Assignments |
|---|---|---|---|---|
| Week 3: Feb 1 - Feb 5 | Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities. | 07 Linear Ridge Regression and Data Partitioning, 08 Sample-by-Sample Linear Regression, 09 Linear Regression with Fixed Nonlinear Features | ||
| Week 4: Feb 8 - Feb 12 | Nonlinear regression with neural networks. | 10 Nonlinear Regression with Neural Networks, 11 More Nonlinear Regression with Neural Networks | 11.1-11.5, 11.7.1, 11.7.4, 11.8.1-11.8.2 | |
| Week 5: Feb 15 - Feb 19 | Autoencoders. Recurrent neural networks. | 12 Autoencoder Neural Networks 13 Recurrent Neural Networks | 11.9, 11.12, 11.14 | A2 Linear Regression with Fixed Nonlinear Features due Monday, Feb 15 at 10:00 PM. |
| Week 6: Feb 22 - Feb 26 | Classification, generative models. | 14 Introduction to Classification | 4.3-4.5, 5.5-5.7 |
March
| Week | Topic | Material | Reading | Assignments |
|---|---|---|---|---|
| Week 7: Feb 29 - Mar 5 | Classification, discriminant models. Ranking. | 10.1-10.4, 10.5-10.10 | A3 Neural Network Regression due Monday, Feb 29 at 10:00 PM. | |
| Week 8: Mar 7 - Mar 11 | Classification with neural networks. | 11.7.2 | ||
| Mar 14 - Mar 18 | Spring Break! | |||
| Week 9: Mar 21 - Mar 25 | Convolutional, bottleneck, and deep networks. | 11.8.3, 11.11, 11.13 | ||
| Week 10: Mar 28 - Apr 1 | Nonparametric methods. | 8.1-8.10 |
April
| Week | Topic | Material | Reading | Assignments |
|---|---|---|---|---|
| Week 11: Apr 4 - Apr 8 | Dimensionality reduction. | 6.1-6.8, 6.10-6.13 | ||
| Week 12: Apr 11 - Apr 15 | Clustering | 7.1-7.10 | ||
| Week 13: Apr 18 - Apr 22 | Support vector machines. | 13.1-13.12 | ||
| Week 14: Apr 25 - Apr 29 | Reinforcement learning. | 18.1-18.9 |
May
| Week | Topic | Material | Reading | Assignments |
|---|---|---|---|---|
| Week 15: May 2 - May 6 | Multiple models. | 17.1-17.12 |
schedule.1456171020.txt.gz · Last modified: 2016/02/22 16:19 (external edit)
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