CS 441 AML - Applied Machine Learning

Coursera Link: https://www.coursera.org/learn/cs441aml

Instructor: Prof. Marco Morales Aguirre

Lead TA: Ehsan Saleh

Course Description

This course is intended for students who want to apply techniques of machine learning to various signal problems. The topics of this course are:

• Classification
• Regression
• High Dimensional Data
• Clustering
• Graphical Models
• Deep Neural Networks

Textbook

• David Forsyth, Applied Machine Learning. Springer International Publishing, 2019. Available at the University of Illinois Library (https://www.library.illinois.edu). Follow the link to ";SpringerLink - Full text online"; to download the PDF. You can use this proxy link to log in the University of Illinois Library with your Illinois credentials and download the book: https://link-springer-com.proxy2.library.illinois.edu/book/10.1007/978-3-030-18114-7.

The following references are also useful:

• Kevin P. Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021. You can find it at the author's website
• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

Syllabus:

You can find the full course syllabus here.

Tutorials and Technical Notes:

1. Technical Note: Accessing the Course in Coursera
2. Technical Note: Rough Estimates of the Implementation Sizes
3. Technical Note: Receiving and Using the Failed Test Cases in Pre-computed Test Databases
4. Technical Note: FAQ about Jupyter Notebooks
5. Technical Note: Lab IDs, and Where to Find Them
6. The Zen of Numpy; Things to Use More (or Less) Often in Numpy
7. Technical Note: Question Posting Guidelines
8. Announcement: All Assignments Are Released