CMPS142, Spring 2015, Section 01: Homework and Reading Assignments

Week 7: (May 12)  PAC bounds (Finite H), Feature selection, Regularization, Perceptron

Reading: Ng's notes, part VI chapter 2 and 3, part VIIb (Perceptron)

Written Homework 4 assigned (due may 19).  Midterm May 21

Week 6: (May 5): Neural networks, Bias Variance decomposition

Reading: Ng's notes part VI, chapter 1, Part VII chapter 1, Backpropagation handout.

Week 5: (apr 30): Finish support vector machines, Nearest Neighbor, Decision trees

Reading: finish Part V, Chapter 9 optional

Written Homework 3 assigned

Week 4: (Apr 21) Finish Generative Models, Naive Bayes, Nearest Neighbor, perhaps start support Vector Machines

Reading: Start reading Part V: support vector machines

Week 3: (Apr 14) multi-class logistic regression, probability review, and start generative models

Read: Part IV (Generative Learning algorithms) of Andrew Ng's notes. 

Written homework 2 assigned (Due Tuesday 2-28)

Week2: Linear regression (continued) and logistic regression

We will skip chapters 8 and 9 of Andrew Ng's notes (Generalized linear models).  Although sections 9.2-9.3 are closely related to the way I will present multi-class logistic regression in class, the notation may be hard to follow without the Generalized Linear models background.

Week 1: Introduction and linear regression

Read Andrew Ng's lecture notes, chapters 1-7.

Written homework 1 (due Tuesday 4/14 4/16) - due date pushed back to give more time for problem 1, and a full week after covering (2-class) logistic regression.

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