## Assignments

**Homework 1** (Due Wed Sep 19th at 3pm in class): In this first homework you will not only learn Python, but while at it, you will also learn to build a search engine!
Read and practice the introduction to python handout and do exercises 2.1, 2.2, 2.3 and 2.4 (please follow the guidelines on how to hand in the homework). To build the small Google search engine, you will need the following webpages:
pages.zip. Please attend the tutorials to learn about python (how to install it, how to program in python, and how to understand what pageRank does). NOTE: This handout and homework are examinable.

**Homework 5**. Due Wednesday November 21st.

## RECOMMENDED READING

- My favourite book for this course is the book of Stuart Russell and Peter Norvig titled artificial intelligence. Chapter 14 covers probabilistic graphical models. Chapter 15 covers HMMs. Chapter 20 talks about maximum likelihood, the EM algorithm, learning the parameters of graphical models and naive Bayes. Chapter 18 teaches decision trees, linear regression, regularization, neural networks and ensemble learning.
- The machine learning book of Hastie, Tibshirani and Friedman is much more advanced, but it is also a great resource and it is free online: The elements of statistical learning.
- For graphical models and Beta-Bernoulli models, I recommend A Tutorial on Learning with Bayesian Networks David Heckerman.
- Kevin Murphy has compiled a nice page about Bayesian learning.
- Wikipedia tutorial on the: SVD
- The following handout should help you with linear algebra.

## MEDIA

- Machine learning video lectures
- Why stats: NYTimes article
- A video lecture about python's package matplotlib
- The machine learning course of Andrew Ng is available in youtube and iTunes. It is strongly recommended.