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====== Schedule ======
----
Video of the lectures will be available via the echo360 portal of the course
|< 100% 18% 40% 19% 13% >|
| ^ Topics ^ Reading ^ Assignments ^
^ Week 1: August 25,27 | | | |
| Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Sections 1.1 and 1.2 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 | | | |
| 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 | |
^ Week 3: September 8,10 | | | |
| Tuesday | Linear regression (continued). Intro to latex | Chapter 3.2 | |
| 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 | Chapter e-7 | [[assignments:assignment4 | Assignment 4]] is available. Due date: 10/30. |
^ Week 9: October 20,22 | | | |
| Tuesday | Neural networks (continued) | Chapter e-7 | |
| Thursday | Deep networks ({{wiki:12_deep_networks.pdf | slides}}) | Chapter e-7 | |
^ Week 10: October 27,29 | | | |
| Tuesday | Deep networks (continued) | Chapter e-7 | |
| Thursday | Features and feature selection ({{wiki:13_features.pdf | slides}}) and here is some code for [[code:feature_selection | feature selection]]. | Chapter e-9 | |
^ Week 11: November 3,5 | | | |
| Tuesday | Principal components analysis ({{wiki:14_pca.pdf | slides}}) | Chapter e-9 | [[assignments:assignment5 | Assignment 5]] is available. Due date: 11/15. |
| Thursday | Nearest neighbor methods ({{wiki:15_distance_based.pdf | slides}}) | Chapter e-6 | |
^ Week 12: November 10,12 | | | |
| Tuesday | Clustering ({{wiki:16_clustering.pdf | slides}}) | Chapter 10 in [[http://www-bcf.usc.edu/~gareth/ISL/ | introduction to statistical learning]] | |
| Thursday | Clustering (cont); stability-based model selection for clustering ({{wiki:17_stability.pdf | slides}}) | A. Ben-Hur, A. Elisseeff and I. Guyon. [[http://psb.stanford.edu/psb-online/proceedings/psb02/benhur.pdf | A stability based method for discovering structure in clustered data]]. Pacific Symposium on Biocomputing, 2002. | |
^ Week 13: November 17,19 | | | |
| Tuesday | Naive Bayes ({{wiki:18_naive_bayes.pdf | slides}}) | | |
| Thursday | Towards the VC dimension ({{wiki:19_vc_dimension.pdf | slides}}) | Chapter 1.3 in the textbook | |
^ Week 14: December 1,3 | | | |
| Tuesday | The VC dimension ({{wiki:20_vc_dimension.pdf | slides}}) | Chapter 2.1,2.2 in the textbook | |
| Thursday | Ensemble methods ({{wiki:21_ensembles.pdf | slides}}) | | |
^ Week 15: December 8,10 | | | |
| Tuesday | Course summary | | |
| Thursday | Poster session | | |