schedule
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schedule [2013/11/19 14:57] asa |
schedule [2016/11/16 10:13] asa |
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| - | Video of the lectures is available via the [[http://echo.colostate.edu:8080/ess/portal/section/0857d976-41e9-4ffd-a18d-144bc57b08ea | 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. |
| - | ===== August ===== | ||
| - | |< 100% 17% 40% 20% 13% >| | + | |< 100% 18% 40% 19% 13% >| |
| | ^ Topics ^ Reading ^ Assignments ^ | | ^ Topics ^ Reading ^ Assignments ^ | ||
| - | ^ Week 1: August 26-30 | | | | | + | ^ Week 1: August 23,25 | | | | |
| - | | Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Prolog and Chapter 1 in the textbook | | | + | | Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Sections 1.1 and 1.2 in the textbook | | |
| - | | Thursday | Course introduction (continued). Short intro to python [ [[notes:python_getting_started | notes]] ]. | Prolog and Chapter 1 | | | + | | Thursday | Course introduction (continued). | Sections 1.1 and 1.2 in the textbook | [[assignments:assignment1| Assignment 1]] is available. | |
| - | + | ^ Week 2: August 30, Sept 1 | | | | | |
| - | + | | 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 | | | |
| - | ===== September ===== | + | | 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 6,8 | | | | | |
| - | |< 100% 17% 40% 20% 13% >| | + | | Tuesday | [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}). | Chapter 3.2 | | |
| - | | ^ Topics ^ Reading ^ Assignments ^ | + | | Thursday | Logistic regression ({{wiki:04_logistic_regression.pdf | slides}}). | Chapter 3.3 | | |
| - | ^ Week 2: Sept 2-6 | | | | | + | ^ Week 4: September 13,15 | | | | |
| - | | Tuesday | Two simple linear models: the closest centroid algorithm and the perceptron algorithm ({{wiki:02_linear.pdf | slides}}) | Chapter 7 | [[assignments:assignment1 | assignment 1]] is out | | + | | Tuesday | Overfitting ({{wiki:05_overfitting.pdf | slides}}) | Chapters 2.3,4.1 | | |
| - | | Thursday | Evaluating and using ML classifiers({{wiki:03_classifier_evaluation.pdf | slides}}). And here's a [[notes:evaluating_classifier_performance | demo]] of the process in PyML. | Chapter 2 | | | + | | Thursday | Regularization and model selection ({{wiki:06_regularization.pdf | slides}}) | Chapter 4 | |
| - | ^ Week 3: Sept 9-13 | | | | | + | ^ Week 5: September 20,22 | | | | |
| - | | Tuesday | Overview of Latex. Go over the code for the [[code:perceptron|perceptron]] classifier. | Chapter 2,7 | | | + | | 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 | Classifier evaluation (continued) | Chapter 2 | | | + | | 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 4: Sept 16-20 | | | | | + | ^ Week 6: September 27,29 | | | | |
| - | | Tuesday | Linear regression ({{wiki:04_linear_regression.pdf | slides}}). | Chapter 7 | | | + | | Tuesday | Large margin classification: support vector machines ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | |
| - | | Thursday | Linear regression - continued (6 slides were added to tuesday's batch). Here's code for [[code:ridge_regression|ridge regression]] that you can try out in PyML. | Chapter 7 | Assignment 1 is due. [[assignments:assignment2 | Assignment 2]] is out | | + | | 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 5: Sept 23-27 | | | | | + | ^ Week 7: October 4,7 | | | | |
| - | | Tuesday | Large margin classifiers: support vector machines ({{wiki:05_svm.pdf | slides}}). | Chapter 7 | | | + | | 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 | support vector machines (continued). | Chapter 7 | | | + | | Thursday | Kernels (continued); [[code:model_selection|model selection]] using grid search | Chapter e-8 | | |
| - | + | ^ Week 8: October 11,13 | | | | | |
| - | ===== October ===== | + | | 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 ({{wiki:11_nn.pdf | slides}}) | Chapter e-7 | | | |
| - | |< 100% 17% 40% 20% 13% >| | + | ^ Week 9: October 18,20 | | | | |
| - | | ^ Topics ^ Reading ^ Assignments ^ | + | | Tuesday | Neural networks (continued); neural network [[code:neural_networks| demo]] | Chapter e-7 | [[assignments:assignment5| Assignment 5]] is available. | |
| - | ^ Week 6: Sept 30 - Oct 4 | | | | | + | | Thursday | Neural networks (continued); deep learning ({{wiki:12_deep_networks.pdf | slides}}) | Chapter e-7 | | |
| - | | Tuesday | SVMs and regularization; SVMs for unbalanced data ({{wiki:05_svm_unbalanced.pdf | slides}}) | A nice tutorial on SVMs: [[http://www.cs.colostate.edu/~asa/pdfs/howto.pdf| A user's guide to support vector machines]]. | | | + | ^ Week 10: October 25,27 | | | | |
| - | | Thursday | Extending SVMs to nonlinear classification ({{wiki:06_kernels.pdf | slides}}). Here's a nice [[http://www.youtube.com/watch?v=3liCbRZPrZA|video]] that illustrates the idea. | Chapter 7 | Assignment 2 is due on Friday | | + | | Tuesday | Deep learning (continued) | Chapter e-7 | | |
| - | ^ Week 7: Oct 7 - 11 | | | | | + | | Thursday | [[code:theano| Theano]]. Features ({{wiki:13_features.pdf | slides}}) | Chapter e-9 | | |
| - | | Tuesday | Kernel classifiers: kernel versions of the perceptron and linear regression ({{wiki:07_kernel_algorithms.pdf | slides}}) and multi-class classification with binary classifiers ({{wiki:08_multi_class.pdf|slides}}) | Chapter 7.5, Chapter 3 | [[assignments:assignment3 | Assignment 3]] is out | | + | ^ Week 11: November 1,3 | | | | |
| - | | Thursday | Evaluating and using ML classifiers: model selection ({{wiki:09_evaluation.pdf | slides}}) | paper on [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.2501&rep=rep1&type=pdf| Dataset selection]] | | | + | | 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]]. | | |
| - | ^ Week 8: Oct 14 - 18 | | | | | + | | Thursday | Principal components analysis (PCA) ({{wiki:14_pca.pdf | slides}}) [[code:pca|demo of pca]] | Chapter e-9 | [[assignments:assignment6| Assignment 6]] is available. | |
| - | | Tuesday | More on kernel functions ({{wiki:10_more_kernels.pdf | slides}}) | | | | + | ^ Week 12: November 8,10 | | | | |
| - | | Thursday | Kernel methods for protein-protein interactions ({{wiki:ppi545.pdf | slides}}) | A. Ben-Hur and W.S. Noble. [[ http://www.cs.colostate.edu/~asa/pdfs/sppii.pdf|Kernel methods for predicting protein-protein interactions]]. Bioinformatics 21(Suppl. 1): i38-i46, 2005. | | | + | | Tuesday | Nearest neighbor methods ({{wiki:15_nearest_neighbors.pdf | slides}}); [[code:nearest_neighbors | demo]]. | Chapter e-6 | | |
| - | ^ Week 9: Oct 21 - 25 | | | | | + | | Thursday | Clustering ({{wiki:16_clustering.pdf | slides}}) kmeans [[http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html | demo]] | Chapter 10 in [[http://www-bcf.usc.edu/~gareth/ISL/ | introduction to statistical learning]] | | |
| - | | Tuesday | Distance based models and nearest neighbor classifiers ({{wiki:11_distances.pdf | slides}}) | Chapter 8 | Assignment 3 is due. [[assignments:assignment4 | Assignment 4]] is out | | + | ^ Week 13: November 15,17 | | | | |
| - | | Thursday | Distance based clustering ({{wiki:12_clustering.pdf | slides}}) | Chapter 8 | | | + | | Tuesday | Naive Bayes classification ({{wiki:18_naive_bayes.pdf | slides}}); [[code:naive_bayes | demo]]. | | | |
| - | ^ Week 10: Oct 28 - Nov 1 | | | | | + | | Thursday | Intro to computational learning theory ({{wiki:19_vc_dimension.pdf | slides}}) | Chapter 1.3 in the textbook | | |
| - | | Tuesday | Probability theory, probabilistic models, and naive Bayes classification ({{wiki:13_naive_bayes.pdf | slides}}) | Chapter 9 | Assignment 4 is due. [[assignments:assignment5 | Assignment 5]] is out | | + | ^ Thanksgiving break | | | | |
| - | | Thursday | Continue discussion of naive Bayes. Obtaining probabilities from linear classifiers ({{wiki:14_callibration.pdf | slides}}) | Chapter 7.4 | | | + | ^ Week 14: November 29, December 1 | | | | |
| - | + | | Tuesday | The VC dimension ({{wiki:20_vc_dimension.pdf | slides}}). | Chapter 2.1, 2.2 in the textbook | | | |
| - | ===== November ===== | + | | Thursday | Ensemble models | | | |
| - | + | ^ Week 15: December 6,8 | | | | | |
| - | |< 100% 17% 40% 20% 13% >| | + | | Tuesday | Course summary | | | |
| - | ^ Week 11: Nov 4 - Nov 8 | | | | | + | | Thursday | Poster session | | | |
| - | | Tuesday | Logistic regression ({{wiki:15_logistic_regression.pdf | slides}}) | Chapter 9 | Assignment 4 is due. [[assignments:assignment5 | Assignment 5]] is out | | + | ^ Week 16 | | | | |
| - | | Thursday | Features and feature selection ({{wiki:16_features.pdf | slides}}) | Chapter 10 | Project proposal is due on friday | | + | | Tuesday | Submit final reports | | | |
| - | ^ Week 12: Nov 11 - Nov 15 | | | | | + | |
| - | | Tuesday | Potential [[feature_selection_bias| bias]] when using feature selection. Principal components analysis (PCA) ({{wiki:17_pca.pdf | slides}}) | Chapter 10 | | | + | |
| - | | Thursday | Decision trees ({{wiki:18_decision_trees.pdf | slides}}) | Chapter 5 | | | + | |
| - | ^ Week 13: Nov 18 - Nov 22 | | | | | + | |
| - | | Tuesday | Ensemble methods ({{wiki:19_ensembles.pdf | slides}}) | Chapter 11 | Assignment 5 is due. | | + | |
| | | ||
schedule.txt · Last modified: 2016/12/05 10:38 by asa
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