CSE 151: Machine Learning -- Schedule

Supplementary references are to the following textbook:
    Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning (2nd edition)
It is available online through Roger and is referred to below as HTF.


Sep 28 Nearest neighbor classification [HTF 2.3, 7.10, 13.3]
Oct 3 Nearest neighbor classification, cont'd
A host of prediction problems [HTF 2.1, 2.2]
Oct 5 Probability review
Homework 1 due
Oct 10 Probability review, cont'd
Introduction to generative modeling
Oct 12 Introduction to generative modeling, cont'd
Homework 2 due
Quiz 1
Oct 17 Linear algebra primer
Oct 19 Gaussian generative models
Homework 3 due
Oct 24 Linear regression [HTF 2.3.1, 3.2, 3.4]
Oct 26 Logistic regression [HTF 4.4]
Homework 4 due
Quiz 2
Oct 31 Optimization primer
Nov 2 Geometry of linear classification [HTF 4.5]
Homework 5 due
Nov 7 Support vector machines [HTF 12.1, 12.2]
Nov 9 Kernels [HTF 12.3]
Homework 6 due
Quiz 3
Nov 14 Kernels, cont'd
Multiclass classification
Nov 16 Decision trees [HTF 9.2]
Homework 7 due
Nov 21 Boosting, bagging, and random forests [HTF 10.1, 10.2, 15.1, 15.2]
Nov 23 No class: Thanksgiving
Nov 28 Clustering [HTF 14.3]
Homework 8 due
Quiz 4
Nov 30 Informative projections [HTF 14.5]
Dec 5 Deep learning [HTF 11]
Dec 7 Deep learning
Homework 9 due
Quiz 5