CSE 151: Machine Learning

Syllabus

I. Prediction problems
        Nearest neighbor
        A taxonomy of prediction tasks

II. Generative models
        The generative approach to classification
        Gaussian generative models

III. Linear prediction
        Linear regression
        Logistic regression
        Perceptron and support vector machines
        Kernels
        Multiclass classification and structured output prediction

IV. Combining simple classifiers
        Decision trees
        Boosting
        Bagging and random forests

V. Representation learning
        Clustering
        Linear projections: PCA and SVD
        Embeddings and manifold learning
        Autoencoders

VI. Deep learning
        Feedforward networks
        Convolutional networks
        Recurrent networks

Discussion sections

Chris: Mon 4-5p in Pepper Canyon 122
Shradha: Thu 7-8p in Center 212

Prerequisites

1. Ability to write simple programs in Python: functions, control structures, string handling, arrays and dictionaries.

2. Familiarity with basic probability, at the level of CSE 21 or CSE 103.

3. Familiarity with basic linear algebra, at the level of Math 18 or Math 20F.

Course materials

1. Programming exercises should be done in Python. I recommend using Jupyter 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).

Homeworks and evaluations

There will be weekly homeworks, to be turned in (typed and in PDF format) on Gradescope. These will be a mix of mathematical exercises and programming projects.

No late homeworks will be accepted; however, the lowest homework score will be dropped.

There will be five in-class quizzes.

Grading

Homeworks: 50% (lowest score will be dropped)
Quizzes: 10% each