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Video of the lectures is available via the echo360 portal of the course. A link will be provided on Canvas.

Topics Reading Assignments
Week 1: August 20-24
Monday Course introduction ( slides). Sections 1.1 and 1.2 in the textbook
Wednesday Course introduction (continued). Sections 1.1 and 1.2 in the textbook
Friday Course introduction and Jupyter notebooks. Assignment 1 is available. Due 9/7.
Week 2: August 27-31
Monday Overview of Numpy ( notebook) and Matplotlib ( notebook). A tutorial on how to write efficient code using Numpy
Wednesday Linear models and the perceptron algorithm ( slides). Chapter 1, and Section 3.1 in the textbook
Friday Linear models (continued). Perceptron notebook.
Week 3: September 4-7
Wednesday Linear regression ( slides). Chapter 3.2
Friday Linear regression (continued). Chapter 3.2 Assignment 2 is available. Due 9/21
Week 4: September 10-14
Monday Logistic regression ( slides). Chapter 3.3
Wednesday Logistic regression (continued). Chapter 3.3
Friday Overfitting ( slides). Chapter 2.3,4.1
Week 5: September 17-21
Monday Overfitting (continued). Regularization ( slides). Chapter 4
Wednesday Regularization (continued). Classifier weights as a function of the regularization parameter ( notebook) Chapter 4
Friday Cross validation ( notebook). Assignment 3 is available. Due 10/5.
Week 6: September 24-28
Monday Measuring classifier accuracy/error ( slides). Notebook on classifier accuracy/error.
Wednesday Support vector machines ( slides). Useful resource: A user's guide to support vector machines. Chapter e-8
Friday Support vector machines (continued). Chapter e-8
Week 7: October 1-5
Monday A deeper look at SVMs ( slides). Code for the svm demo.
Wednesday Nonlinear SVMs: kernels ( slides). Chapter e-8
Friday Kernels (continued). Chapter e-8 Assignment 4 is available. Due 10/19.
Week 8: October 8-12
Monday Classifier evaluation ( slides).
Wednesday Model selection in scikit-learn notebook.
Friday Kernels (continued).
Week 9: October 15-19
Monday Neural networks ( slides). notebook. Chapter e-7
Wednesday Neural networks (continued). Chapter e-7
Friday Neural networks (continued). Chapter e-7 Project proposals due Nov 3rd.
Week 10: October 22-26
Monday Neural networks (continued). Chapter e-7 Assignment 5 is available. Due 11/6
Wednesday Neural networks (continued) (updated slides). Chapter e-7
Friday Deep networks ( slides). Chapter e-7
Week 11: October 29 - November 2
Monday PyTorch. tensors and neural networks Also see the PyTorch tutorials.
Wednesday Convolutional networks.
Friday Convolutional networks in PyTorch. notebook.
Week 12: November 4 - 9
Monday Deep learning: auto-encoders ( slides). Multi-class classification ( slides); a demo of multi-class classification Chapter e-7
Wednesday Features: selection and scaling ( slides) Chapter e-9
Friday Feature selection (continued) notebook Chapter e-9
Week 13: November 11 - 16
Monday Principal component analysis (PCA) ( slides). notebook Chapter e-9
Wednesday Nearest neighbor classification ( slides). Chapter e-9
Friday Nearest neighbor classification notebook . Chapter e-9
Thanksgiving week
Week 14: November 26 - 30
Monday Discussion of final report. Final report due Wednesday Dec 12th
Wednesday Clustering ( slides). notebook Chapter 10 in introduction to statistical learning
Friday Clustering (continued). Chapter 10 in introduction to statistical learning
Week 15: December 3 - 7
Monday Ensemble methods ( slides). notebook
Wednesday Course summary ( slides). notebook
Friday Poster session
Week 16
Wednesday Submit final reports
schedule.txt · Last modified: 2018/11/27 10:52 by asa