The lab component of the course will cover mathematical concepts used in computational linguistics. The purpose of the lab is to familiarize you with not-so-basic probability theory, information theory, Bayesian inference, linear algebra, and descriptive and inferential statistics. These concepts are crucial in understanding computational linguistics and natural language processing algorithms covered in lecture. If you are shaky about these topics, you are recommended to attend the lab and do the exercise. If you are going to take CS134 next year (Statistical Natural Language Processing), the lab is highly recommended.

Lab instructor : Te Rutherford

Place and Time : Volen 119 Tuesday 6:30PM – 7:30PM

**Lab notes and exercises**

You are encouraged but not required to complete and turn in the exercises. But to make the encouragement more tangible, we will offer extra credit for every exercise you turn in. The exercises already include the answer key for you, so it is left to you to show the work that leads you to the answer.

Notes and exercises from the lab will be posted here as the semester progresses.

- Probability Theory I : (Tabular) Probability Distribution Function: Joint, Conditional, and Marginal Probabilities
- Information Theory I : Entropy and Mutual Information | exercise | slides
- Probability Theory II : Maximum Likelihood Estimation vs Bayesian Estimation | code | exercise
- Probability Theory III : Bayesian Inference | exercise
- Probability Theory IV : Markov Chain (and summation pushing) | exercise
- Linear Algebra I : Vector operations | exercise
- Linear Algebra II : Matrix operations
- Descriptive statistics
- Inferential statistics