# Additional Resources for ECE 435 - Machine Learning and Pattern Recognition

Posted on by Tejas Gupta

Category: 2023 guide Princeton

Probably the most important class that you’ll take as anyone working with data.

# What is ECE 435?

The course name Machine Learning and Pattern Recognition is a very vague notion. In concrete terms, this course is a graduate-level (no really, it’s cross listed as ECE 535 and majority graduate students) **proof-based data-driven machine learning course**.

# When Should You Take It?

As soon as possible, as long as you have a solid grasp of linear algebra, multivariable calculus, and probability (whether or not you have actually taken those courses). If you want to take courses, I would recommend MAT 217, MAT 201, ORF 309 for requisite knowledge, but that might be overkill. I say take this course as soon as possible because a solid mathematical foundation of data-driven machine learning algorithms will make all the concepts much, much more clear and allow you to apply these skills anywhere.

You may think that you understand ML from a COS 324-style hand-wavy machine learning course, but I didn’t, and you don’t.

# Additional Resources

- For linear algebra and visualization: 3Blue1Brown’s Linear Algebra series
- For Singular Value Decomposition, Principal Component Analysis, and Linear Regression: Eigensteve’s SVD Playlist
- For Gaussian Mixture Models and Expectation Maximization, Ritvikmath’s videos
- For Maximum Likelihood Expectation: see StatQuest

# My Advice and Notes

*To Be Added post-completion*