# Resources

These lists are by no means comprehensive.

## Professional opportunities

## Academic job search

This process is far from over for me, but here are some websites that I used to find jobs in ECE, CS, and applied math.

## Courses

I somehow ended up taking a ridiculous number of classes in grad school.

#### Signal processing/statistics/machine learning (ML) Theory

- 10715 Advanced Intro to ML
- 10716 Advanced ML Theory
- 36705 Intermediate Statistics
- 36709 Advanced Statistical Theory I: High-dimensional Statistics
- 36741 Statistics meets Optimization: Random Sketching
- 10725 Convex Optimization
- 10708 Probabilistic Graphical Models
- 18898G Sparsity, Structure, Inference
- 6.860 Statistical Learning Theory and Applications (MIT)
- 6.252 Discrete Stochastic Processes (MIT)

#### Biomedical ML/statistics/signal processing

- 36661 Statistical Methods in Epidemiology (audit)
- 36759 Statistical Models of the Brain
- 6.S897 ML for Healthcare (MIT)
- 6.872 Biomedical Computing (MIT)

#### Other signal processing/ML applications

- 18667 Algorithms for Large-scale Distributed ML and Optimization (audit)
- 10703 Deep Reinforcement Learning and Control
- 11785 Intro to Deep Learning
- 16720 Computer Vision
- 18793 Image and Video Processing

## Other technical readings

Things I read on my own (or with friends) that I enjoyed.

- UCLA EDUC 260A R class
- All of statistics by Wasserman
- Information Theory, Inference, and Learning Algorithms by MacKay
- Introduction to Graph Theory (Coursera)
- Deep Learning by Courville, Goodfellow, Bengio
- MIT 6.050J Information, Entropy, and Computation notes