I. Nonparametric methods
II. Classification using parametrized models
Generative models: naive Bayes, multivariate Gaussian, Fisher linear discriminant
Discriminative models: logistic regression
More linear classifiers: Perceptron, support vector machines
Richer output spaces: multiclass classification and structured output prediction
III. Combining classifiers
Mixtures of experts and multiplicative updates
Boosting, bagging, and random forests
IV. Representation learning
Linear projections: PCA and SVD
Embeddings and manifold learning
Xinan: Mon 7-8 in WLH 2204
Sharad: Wed 7-8 in WLH 2204
Dev: Wed 8-9 in WLH 2204
1. Ability to write simple programs in Python: functions, control structures, string handling, arrays and dictionaries
2. Familiarity with basic probability
3. Familiarity with basic linear algebra
1. Programming exercises should be done in Python. I recommend trying out iPython notebooks.
2. There is no required text for the course. But here are some useful references. The first is available as an e-book through the library website; the rest are on reserve at Geisel:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The elements of statistical learning (2nd edition).
Gilbert Strang. Linear algebra and its applications .
Kevin Murphy, Machine learning: a probabilistic perspective.
Richard Duda, Peter Hart, and David Stork, Pattern classification (2nd edition).
There will be weekly homeworks, to be turned in (typed and in PDF format) on Gradescope. No late homeworks will be accepted; however, the lowest homework score will be dropped.
Midterms: TBA, in class
Homeworks: 50% (lowest score will be dropped)
Midterms: 25% each