Theoretical take on GLMs. Does not have a lot of concrete data examples.

Recommended books by the UC Berkeley SGSA. Source.

- Applied Statistics - Categorical Data
- Applied Statistics - Linear Models
- Applied Statistics - Machine Learning
- Theoretical Statistics
- Probability - Undergraduate Level
- Probability - Measure Theoretic
- Probability - Stochastic Calculus
- Probability - Random Walk and Markov Chains
- Mathematics - Convex Optimization
- Mathematics - Linear Algebra
- Mathematics - Convex Analysis
- Mathematics - Measure Theory
- Mathematics - Combinatorics
- Computational Biology
- Population Genetics
- Computer Science - Numerical Analysis
- Computer Science - Algorithms

Theoretical take on GLMs. Does not have a lot of concrete data examples.

...Berkeley classic!

Classic, approachable text, [Available online.]

[Available online.]

The primary text for Stat 210A. [Download from SpringerLink.]

A good reference for Stat 210A.

A good reference for Stat 210A.

Some students find this helpful to supplement the material in 210B.

What the majority of Berkeley undergraduates use to learn probability.

What students in EECS use to learn about randomized algorithms and applied probability.

These are indispensable tools of probability. Some nice references are

[Available online.]

Great overview of sequencing technology for the unacquainted.

Well-written, go-to reference for all things involving categorical data.