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:
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.
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.