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| ^ Topics ^ Reading ^ Assignments ^ | | ^ Topics ^ Reading ^ Assignments ^ | ||
^ Week 1: August 25,27 | | | | | ^ Week 1: August 25,27 | | | | | ||
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| Thursday | Course introduction (continued). Linear models and the perceptron algorithm ({{wiki:02_linear.pdf | slides}}) | Chapters 1,3.1 in the textbook | | | | Thursday | Course introduction (continued). Linear models and the perceptron algorithm ({{wiki:02_linear.pdf | slides}}) | Chapters 1,3.1 in the textbook | | | ||
^ Week 2: September 1,3 | | | | | ^ Week 2: September 1,3 | | | | | ||
- | | Tuesday | Linear models (continued). Short intro to python [ [[notes:python_getting_started | notes]] ] | Chapters 1,3.1 in the textbook | | | + | | Tuesday | Linear models (continued). Short intro to python [ [[notes:python_getting_started | notes]] ] | Chapters 1,3.1 in the textbook | [[assignments:assignment1 | Assignment 1]] is available. Due date: 9/17. | |
| Thursday | More Python; [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}) | Chapter 3.2 | | | | Thursday | More Python; [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}) | Chapter 3.2 | | | ||
^ Week 3: September 8,10 | | | | | ^ Week 3: September 8,10 | | | | | ||
- | | Tuesday | Linear regression (continued). Intro to latex | Chapters 3.2 | | | + | | Tuesday | Linear regression (continued). Intro to latex | Chapter 3.2 | | |
| Thursday | Logistic regression ({{wiki:04_logistic_regression.pdf | slides}}) | Chapter 3.3 | | | | Thursday | Logistic regression ({{wiki:04_logistic_regression.pdf | slides}}) | Chapter 3.3 | | | ||
- | + | ^ Week 4: September 15,17 | | | | | |
- | | + | | Tuesday | Overfitting ({{wiki:05_overfitting.pdf | slides}}) | Chapters 2.3,4.1 | | |
+ | | Thursday | Regularization and model selection; cross validation ({{wiki:06_regularization.pdf | slides}}) | Chapter 4.2, 4.2.2 | [[assignments:assignment2 | Assignment 2]] is available. Due date: 10/2. | | ||
+ | ^ Week 5: September 22,24 | | | | | ||
+ | | Tuesday | Support vector machines ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | | ||
+ | | Thursday | SVMs (continued) | Chapter e-8 | | | ||
+ | ^ Week 6: September 29, October 1 | | | | | ||
+ | | Tuesday | Expressing SVMs in terms of error + regularization; unbalanced data ({{wiki:07_svm_unbalanced.pdf | slides}}). Here's [[code:demo2d | code]] for displaying the decision boundary of a classifier. | Chapter e-8 | | | ||
+ | | Thursday | Nonlinear SVMs: kernels ({{wiki:08_kernels.pdf | slides}}) | Chapter e-8 | [[assignments:assignment3 | Assignment 3]] is available. Due date: 10/16. | | ||
+ | ^ Week 7: October 6,8 | | | | | ||
+ | | Tuesday | Kernels continued; model selection ({{wiki:09_evaluation.pdf | slides}}); a [[code:model_selection | demo]] of model selection in scikit-learn. | Chapter e-8 | | | ||
+ | | Thursday | Multi-class classification ({{wiki:10_multi_class.pdf | slides}}). And here's [[code:multi_class | how to do it]] in scikit-learn. | | | | ||
+ | ^ Week 8: October 13,15 | | | | | ||
+ | | Tuesday | Neural networks and the backpropagation algorithm ({{wiki:11_nn.pdf | slides}}) | Chapter e-7 | | | ||
+ | | Thursday | Neural networks (continued) code for [[code:neural_network | neural networks]] trained using backpropagation | [[assignments:assignment4 | Assignment 4]] is available. Due date: 10/30. | | | ||
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