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schedule [CS545 fall 2015]

CS545 fall 2015

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CS545


Instructor
Asa Ben-Hur



http://www.colostate.edu

schedule

Schedule

Video of the lectures will be available via the echo360 portal of the course

Topics Reading Assignments
Week 1: August 25,27
Tuesday Course introduction ( slides). Sections 1.1 and 1.2 in the textbook
Thursday Course introduction (continued). Linear models and the perceptron algorithm ( slides) Chapters 1,3.1 in the textbook
Week 2: September 1,3
Tuesday Linear models (continued). Short intro to python [ notes ] Chapters 1,3.1 in the textbook Assignment 1 is available. Due date: 9/17.
Thursday More Python; code for the perceptron. Linear regression ( slides) Chapter 3.2
Week 3: September 8,10
Tuesday Linear regression (continued). Intro to latex Chapter 3.2
Thursday Logistic regression ( slides) Chapter 3.3
Week 4: September 15,17
Tuesday Overfitting ( slides) Chapters 2.3,4.1
Thursday Regularization and model selection; cross validation ( slides) Chapter 4.2, 4.2.2 Assignment 2 is available. Due date: 10/2.
Week 5: September 22,24
Tuesday Support vector machines ( 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 ( slides). Here's code for displaying the decision boundary of a classifier. Chapter e-8
Thursday Nonlinear SVMs: kernels ( slides) Chapter e-8 Assignment 3 is available. Due date: 10/16.
Week 7: October 6,8
Tuesday Kernels continued; model selection ( slides); a demo of model selection in scikit-learn. Chapter e-8
Thursday Multi-class classification ( slides). And here's how to do it in scikit-learn.
Week 8: October 13,15
Tuesday Neural networks and the backpropagation algorithm ( slides) Chapter e-7
Thursday Neural networks (continued) code for neural networks trained using backpropagation Chapter e-7 Assignment 4 is available. Due date: 10/30.
Week 9: October 20,22
Tuesday Neural networks (continued) Chapter e-7
Thursday Deep networks ( slides) Chapter e-7
Week 10: October 27,29
Tuesday Deep networks (continued) Chapter e-7
Thursday Features and feature selection ( slides) and here is some code for feature selection. Chapter e-9
Week 11: November 3,5
Tuesday Principal components analysis ( slides) Chapter e-9 Assignment 5 is available. Due date: 11/15.
Thursday Nearest neighbor methods ( slides) Chapter e-6
Week 12: November 10,12
Tuesday Clustering ( slides) Chapter 10 in introduction to statistical learning
Thursday Clustering (cont); stability-based model selection for clustering ( slides) A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing, 2002.
Week 13: November 17,19
Tuesday Naive Bayes ( slides)
Thursday Towards the VC dimension ( slides) Chapter 1.3 in the textbook
Week 14: December 1,3
Tuesday The VC dimension ( slides) Chapter 2.1,2.2 in the textbook
Thursday Ensemble methods ( slides)
Week 15: December 8,10
Tuesday Course summary
Thursday Poster session
schedule.txt · Last modified: 2015/12/01 11:52 by asa

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