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