CIS 520 provides a fundamental introduction to the mathematics, algorithms and practice of machine learning, focusing on representation, loss functions on optimization. Topics covered include:

**Supervised learning**: least squares regression, logistic regression, feature selection/regularization, online learning, boosting, naive Bayes, support vector machines, ensemble methods, neural nets/deep learning**Unsupervised learning**: PCA, K-means clustering, Gaussian Mixture Models, EM, HMMs, Bayesian networks**Reinforcement learning**: TD-learning, Q-learning, deep learning

The course is aimed broadly at advanced undergraduates and beginning graduate students in computer science, electrical engineering, mathematics, physics, and statistics. This is a hard course; A good alternative for those with less math background or time is CIS419/519 or, if you want a really nice, much easier intro, take the Coursera ML course. If unsure which to take, see this.

We will be coding in Python, using the Jupyter/SKLearn/Pytorch libraries. All are open source and can be run on your machine or on Google Colab.

- Basic algorithms, data structures and complexity. [CSE 320]
- Basic probability and statistics (random variables, moments, standard distributions, simple regression). [CSE 261]
- Basic linear algebra (matrices, vectors, norms, inverses).
- Reasonable programming skills, including basic knowledge of python.

- 8 Problem Sets: 40% - Your lowest homework score will be dropped. Any homework turned in late will be penalized 25 points per late day or fraction of day.
- Midterm: 20%
- Project: 12% - Late days are not permitted for the final project.
- Quizzes 3% -
*Quizzes are not graded*— just marked as completed or not. - Final: 25%

The problem sets include programming questions. The midterm and final will be semi-closed book exams (cheat sheet allowed), which will encompass material covered in the lectures and assigned in the readings. For the project is an open-ended three person team project.

We do not take attendance, but you will learn more if you attend lectures instead of watching the recordings.

- Semi-Optional Text: C. Bishop, Pattern Recognition and Machine Learning. 2007
- See also Resources and Lectures