This repository provides instructional material for machine learning in python. The material is used for graduate class taught at NYU Tandon by Pei Liu. Most of the materials are from Sundeep Rangan, who taught this course in previous semesters.
- EL-GY 6143: Introduction to Machine Learning (Graduate)
Anyone is free to use and copy this material (at their own risk!). But, please cite the material if you use the material in your own class.
- All the software can be run on any laptop (Windows, MAC or UNIX). Instructions are also provided to run the code in Google Cloud Platform on a virtual machine (VM).
- If you want to avoid setting up software on your local machine, most of the demos can also be directly run in the cloud on the excellent Google Colaboratory cloud service.
- Both the undergrad and graduate classes assume no python or ML experience. However, experience with some programming language (preferably object-oriented) is required.
- To follow all the mathematical details and to complete the homework exercises, the class assumes undergraduate probability, linear algebra and multi-variable calculus.
Go to the units sequence for all the material for the machine learning course divided into units. Each unit includes slides, python demos, problems and labs.
Any feedback is welcome. If you find errors, have ideas for improvements, or want to voice any other thoughts, create an issue and we will try to get to it. Even better, fork the repository, make the changes yourself and create a pull request and we will try to merge it in. See the excellent instructions from the former TA Ish Jain.
The course material has been developed by several faculty including:
- Sundeep Rangan, ECE, NYU (Main developer)
- Yao Wang, ECE, NYU
- Alyson K. Fletcher, Statistics, ECE, UCLA
