| # |
Day |
Topic/Slides |
Reading |
| 1 | M
8/26 | Introduction | FCML Ch1, ISLR Ch1 |
| 2 | W
8/28 | The Linear Model and Least Mean Squares |
|
| M
9/2 | Labor Day |
| 3 | W
9/4 | Higher Dimensions |
|
| 4 | M
9/9 | Geometry of LLMS, Nonlinear response |
| 5 | W
9/11 | Cross Validation, model selection, regularization | ISLR Ch 5 |
|
| 6 | M
9/16 | Probability and Expectation | FCML Ch 2, ISLR Ch 2 |
| 7 | W
9/18 | More Probability |
|
| 8 | M
9/23 | Linear Gaussian Model |
| 9 | W
9/25 | Maximum Likelihood |
|
| 10 | M
9/30 | Properties of Linear Gaussian Model I |
| 11 | W
10/2 | Properties of Linear Gaussian Model II |
|
| 12 | M
10/7 | Introduction to Bayesian Modeling | FCML Ch 3 |
| 13 | W
10/9 | Priors and Marginal Likelihood |
|
| 14 | M
10/14 | Bayesian Linear Gaussian Model |
| 15 | W
10/16 | Marginal Likelihood Model Selection |
|
| 16 | M
10/21 | Review |
| 17 | W
10/23 | Midterm Exam |
|
| 18 | M
10/28 | Logistic Regression | FCML Ch 4, ISLR Ch 4 |
| 19 | W
10/30 | Estimation I - Gradient Methods |
|
| 20 | M
11/4 | Estimation II - Laplace Approximation |
| 21 | W
11/6 | Estimation III - Sampling, Metropolis-Hastings |
|
| 22 | M
11/11 | Veterans Day |
| 23 | W
11/13 | Classification - Bayesian Classifier | FCML Ch 5 |
|
| 24 | M
11/18 | Classification - Nearest Neighbors, Classifier Evaluation |
| 25 | W
11/20 | Classification - SVMs I - Maximum Margin |
|
| 26 | M
11/25 | Classification - SVMs II - Kernels |
| 27 | W
11/27 | Neural Networks I - Perceptron and Backpropagation | TBA |
|
| 28 | M
12/2 | Neural Networks II - Autoencoders |
| 29 | W
12/4 | Clustering - Kmeans and Mixture Models | FCML Ch 6 |
|
| 30 | M
12/9 | Clustering - Gaussian Mixture Model and EM |
| 31 | W
12/11 | Principle Components Analysis | FCML Ch 7 |
|
| M
12/13 | Final Assignment Due (no in-class exam)
|