CSE 250B: Machine Learning -- Lecture schedule

Supplementary references are given for each lecture, either to research papers or to the following textbooks:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The elements of statistical learning (2nd edition). This is available online through Roger and is referred to below as HTF.
Kevin Murphy, Machine learning: a probabilistic perspective.
Richard Duda, Peter Hart, and David Stork, Pattern classification (2nd edition).


Course overview (Jan 5)
HTF 1, 2.1, 2.2

Part I: Nonparametric methods

Nearest neighbor classification (Jan 7)
HTF 2.3, 2.4, 2.5, 13.3

Decision trees (Jan 12)
HTF 9.2

Part II: Classification with parametrized models

Classification with generative models (Jan 14, 19, 21, 26)
HTF 4.3

Classification with discriminative models (Jan 28)
HTF 4.4

More linear classification (Feb 2,9)
HTF 4.5, 12.2

Kernels (Feb 9,11)
HTF 12.3

Richer output spaces (Feb 16)

Part III: Combining classifiers

Boosting, bagging, and random forests (Feb 18)
HTF 10.1, 10.4, 15

Part IV: Representation learning

Clustering (Feb 23)
HTF 13.2, 14.1, 14.3

Informative projections (Feb 25)
HTF 14.5.1, 14.6, 14.7

Embeddings, manifold learning, and dictionary learning (Mar 1)
HTF 14.8, 14.9

Part V: Other models of learning

Prediction with expert advice (Mar 3)

Semisupervised and active learning (Mar 8)