Assignments

Math Homeworks

There will be one math homework (HW) for each topical unit of the course.

These are intended to build your skills in formal mathematics (analysis, derivation).

• HW0: Probability Fundamentals
• HW1: Beta, Dirichlet, and Estimators of Unigram Probability
• HW2: Gaussians and Estimators
• HW3: Sampling, Markov Chains, and MCMC
• HW4: K-means and Gaussian Mixtures
• HW5: Hidden Markov Models

Coding Practicals

There will be one coding practical (CP) for each topical unit of the course.

These are intended to build your implementation skills in Python, and will often be tightly intertwined with the same unit's math homework.

• CP1: Unigram Probabilities
• CP2: Bayesian Linear Regression
• CP3: Implementing Markov Chain Monte Carlo
• CP4: Coordinate Ascent for Gaussian Mixtures
• CP5: Dynamic Programming for Hidden Markov Models

Project

In the last half of the course, you'll complete a open-ended team project.

See our Project Overview page.

• Deliverable 1: Team Formation
• Deliverable 2: Initial Report
• Deliverable 3: Chat with Course Staff
• Deliverable 4: Final Report

Quizzes

Short in-class quizzes will be given after each unit of the course.

These are primarily designed to verify your mathematical and conceptual understanding. You can view each homework as good practice for the corresponding quiz.

See the Schedule for the scheduled dates of each quiz.