There will be three problems (8 points each) and three simple exercises (3 points each). Thus if you solve any two problems + all exercises, you will still get the maximum grade. If you do everything, you will get 8 bonus points.

The material covered in the following lectures will NOT be on the midterm:

- Generalization bounds (the lecture before the midterm)
- Reductions
- Sanjoy's talk on projections, Gaussian scale mixtures, and the EM

The other two problems will be on any two of the following algorithms:

- Winnow
- Halving
- (Randomized) Weighted Majority
- Perceptron

## 6 comments:

A student asks: "Will the problem questions be of the nature of deriving proofs or application based"

Reply: You won't have to produce proofs, but I may ask you to produce a sequence of examples that forces a given algorithm to make a certain number of mistakes.

How long will the exam take?

2:40-3:55pm

hello!

is it possible to post some practice problems for the exam?

thanks!

That's what homeworks were for!

A good way to find practice problems is to go to other Machine Learning classes and check out their homework assignments and exams (e.g., http://courses.csail.mit.edu/6.867/exams.html).

Post a Comment