schedule
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schedule [2015/12/01 11:52] asa |
schedule [2016/11/03 14:22] asa |
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| - | Video of the lectures will be available via the echo360 portal of the course | + | Video of the lectures is available via the echo360 portal of the course. A link is provided on Canvas and Piazza. |
| |< 100% 18% 40% 19% 13% >| | |< 100% 18% 40% 19% 13% >| | ||
| | ^ Topics ^ Reading ^ Assignments ^ | | ^ Topics ^ Reading ^ Assignments ^ | ||
| - | ^ Week 1: August 25,27 | | | | | + | ^ Week 1: August 23,25 | | | | |
| | Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Sections 1.1 and 1.2 in the textbook | | | | 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 | | | + | | Thursday | Course introduction (continued). | Sections 1.1 and 1.2 in the textbook | [[assignments:assignment1| Assignment 1]] is available. | |
| - | ^ Week 2: September 1,3 | | | | | + | ^ Week 2: August 30, Sept 1 | | | | |
| - | | 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. | | + | | Tuesday | Linear models ({{wiki:02_linear.pdf | slides}}). Short intro to LaTex and python [ [[notes:python_getting_started | notes]] ]. | Chapter 1, and Section 3.1 in the textbook | | |
| - | | Thursday | More Python; [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}) | Chapter 3.2 | | | + | | Thursday | Linear models and the perceptron algorithm (cont). | Chapter 1, and Section 3.1 in the textbook | [[assignments:assignment2| Assignment 2]] is available. | |
| - | ^ Week 3: September 8,10 | | | | | + | ^ Week 3: September 6,8 | | | | |
| - | | Tuesday | Linear regression (continued). Intro to latex | Chapter 3.2 | | | + | | Tuesday | [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}). | 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 | | | | | + | ^ Week 4: September 13,15 | | | | |
| | Tuesday | Overfitting ({{wiki:05_overfitting.pdf | slides}}) | Chapters 2.3,4.1 | | | | 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. | | + | | Thursday | Regularization and model selection ({{wiki:06_regularization.pdf | slides}}) | Chapter 4 | |
| - | ^ Week 5: September 22,24 | | | | | + | ^ Week 5: September 20,22 | | | | |
| - | | Tuesday | Support vector machines ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | | + | | Tuesday | Model selection and cross validation (continued). Code for [[code:cross_validation | cross validation]] in scikit-learn | Chapter 4 | [[assignments:assignment3| Assignment 3]] is available. | |
| - | | Thursday | SVMs (continued) | Chapter e-8 | | | + | | Thursday | Discussion of classifier evaluation and metrics for classifier accuracy; here's the code for computing/plotting [[code:roc|ROC curves]]. Short intro to large margin classification ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | |
| - | ^ Week 6: September 29, October 1 | | | | | + | ^ Week 6: September 27,29 | | | | |
| - | | 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 | | | + | | Tuesday | Large margin classification: support vector machines ({{wiki:07_svm.pdf | slides}}) | 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. | | + | | Thursday | The dual for the hard margin and soft margin SVM ({{wiki:07_svm.pdf | slides}}); [[code:demo2d|svm demo]]; Expressing SVMs in terms of error + regularization ({{wiki:07_svm_unbalanced.pdf | slides}}) | Chapter e-8 | | |
| - | ^ Week 7: October 6,8 | | | | | + | ^ Week 7: October 4,7 | | | | |
| - | | Tuesday | Kernels continued; model selection ({{wiki:09_evaluation.pdf | slides}}); a [[code:model_selection | demo]] of model selection in scikit-learn. | Chapter e-8 | | | + | | Tuesday | SVMs for unbalanced data ({{wiki:07_svm_unbalanced.pdf | slides}}) Nonlinear classification with kernels ({{wiki:08_kernels.pdf | slides}}) | Chapter e-8 | [[assignments:assignment4| Assignment 4]] is available. | |
| - | | Thursday | Multi-class classification ({{wiki:10_multi_class.pdf | slides}}). And here's [[code:multi_class | how to do it]] in scikit-learn. | | | | + | | Thursday | Kernels (continued); [[code:model_selection|model selection]] using grid search | Chapter e-8 | | |
| - | ^ Week 8: October 13,15 | | | | | + | ^ Week 8: October 11,13 | | | | |
| - | | Tuesday | Neural networks and the backpropagation algorithm ({{wiki:11_nn.pdf | slides}}) | Chapter e-7 | | | + | | Tuesday | Model selection ({{wiki:09_evaluation.pdf | slides}}). Multi-class classification ({{wiki:10_multi_class.pdf | slides}}), a [[code:multi_class| demo]] of multi-class classification | Chapter 4.3.3 | | |
| - | | 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. | | + | | Thursday | Neural networks ({{wiki:11_nn.pdf | slides}}) | Chapter e-7 | | |
| - | ^ Week 9: October 20,22 | | | | | + | ^ Week 9: October 18,20 | | | | |
| - | | Tuesday | Neural networks (continued) | Chapter e-7 | | | + | | Tuesday | Neural networks (continued); neural network [[code:neural_networks| demo]] | Chapter e-7 | [[assignments:assignment5| Assignment 5]] is available. | |
| - | | Thursday | Deep networks ({{wiki:12_deep_networks.pdf | slides}}) | Chapter e-7 | | | + | | Thursday | Neural networks (continued); deep learning ({{wiki:12_deep_networks.pdf | slides}}) | Chapter e-7 | | |
| - | ^ Week 10: October 27,29 | | | | | + | ^ Week 10: October 25,27 | | | | |
| - | | Tuesday | Deep networks (continued) | Chapter e-7 | | | + | | Tuesday | Deep learning (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 | | | + | | Thursday | [[code:theano| Theano]]. Features ({{wiki:13_features.pdf | slides}}) | Chapter e-9 | | |
| - | ^ Week 11: November 3,5 | | | | | + | ^ Week 11: November 1,3 | | | | |
| - | | Tuesday | Principal components analysis ({{wiki:14_pca.pdf | slides}}) | Chapter e-9 | [[assignments:assignment5 | Assignment 5]] is available. Due date: 11/15. | | + | | Tuesday | Feature selection ({{wiki:13_features.pdf | slides}}); feature selection[[[code:feature_selection| demo]]. | [[http://www.jmlr.org/papers/v3/guyon03a.html | Introduction to Variable and Feature Selection]]. | | |
| - | | Thursday | Nearest neighbor methods ({{wiki:15_distance_based.pdf | slides}}) | Chapter e-6 | | | + | | Thursday | Principal components analysis (PCA) ({{wiki:14_pca.pdf | slides}}) [[code:pca|demo of pca]] | Chapter e-9 | | |
| - | ^ 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. | | | + | |< 100% 18% 40% 19% 13% >| |
| - | ^ Week 13: November 17,19 | | | | | + | |
| - | | Tuesday | Naive Bayes ({{wiki:18_naive_bayes.pdf | slides}}) | | | | + | ^ Week 15: December 6,8 | | | | |
| - | | 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 | | | | | Tuesday | Course summary | | | | ||
| | Thursday | Poster session | | | | | Thursday | Poster session | | | | ||
schedule.txt · Last modified: 2016/12/05 10:38 by asa
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