CIS-4320/5320 (Machine Learning) Home Page

This is the home page for Peter Chapin's Machine Learning course notes for the Fall 2022 semester. Here you will find electronic versions of class handouts, homework assignments, lecture slides, and links to other references of interest. If you are a student taking Machine Learning consider bookmarking this page.

The course syllabus gives an overview of the course and its content, lists course resources, and describes the grading policy and related issues.
The homework submission area and grade book are on Canvas (4320, 5320), but all other course resources are here.
The Zoom meeting URL gives you access to the live lectures and the lab sessions.
(Optional) You can download and install Python for your platform from the main Python website. This site has links to a lot of educational material as well. Installing Python this way is optional because Anaconda (below) comes with its own Python interpreter.
We will be using Anaconda and Visual Studio Code to write some programs for this course. I have prepared some instructions about how to set up those tools.
I will distribute sample code via a Git repository on GitHub. You can use VSCode to clone that repository to your machine and then use the features of Git to manage your own branches, etc. I will discuss how to do this in class.
There are many Python books available in the O'Reilly eBook Collection via the Hartness Library. Start with Learning Python, Fifth Edition by Mark Lutz, particular if you are entirely new to Python.
I've prepared some general information on submitting assignments.
My home page contains other resources of potential interest.

Lecture Topics

Slides

Introduction. These slides are a work in progress.

Homework

Here is a checklist of things to do with VSCode to see if you are able to work with the tool, and with Git, in an effective way. This is not a graded activity. It is just intended to give your exploration of VSCode a little structure.

Resources/Articles

The Python Home Page contains documentation, tutorials, and a place to download the official ("stock") version of Python.
A nice description on how to include documentation in your Python programs.
A tutorial on PyTest.
How to change your Jupyter Notebook start-up folder. This is a Stack Overflow answer that seems to work.