Textbooks

Table of Contents

1. Mathematics
   1. Calculus
   2. Linear Algebra
   3. Probability + Statistics
      1. Math Perspective
      2. Computer Science + Machine Learning Perspective
      3. Bayesian Statistics
   4. Mathematics for Machine Learning and Data Driven Problems
2. Machine Learning
   1. Introduction
   2. Statistical Perspective
   3. Probabilistic Perspective
   4. Variational Inference
      1. Variational Inference Explainations
   5. Pattern Matching
   6. Deep Learning
3. Applied Machine Learning
   1. Computer Vision
   2. Natural Language Processing + Information Retrieval
4. Visualization
5. Creative Coding
6. Systems Programming
7. Programming Language Theory
8. Engineering - Software + Systems
9. Engineering - Machine Learning + AI
10. Programming Languages + Tools
    1. Tools
    2. Machine Learning
    3. Systems Programming
       1. Rust
       2. Golang
    4. Web + The Internet
    5. Data Analysis
       1. R
       2. Python
    6. Database Systems
11. Technical Inteviews

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Mathematics

Calculus

• Calculus by James Stewart - any edition works

Linear Algebra

• Linear Algebra and Its Applications
• Introduction to Linear Algebra

Probability + Statistics

Math Perspective

• Introduction to Probability

  Computer Science + Machine Learning Perspective

• Probability for Computer Science - Stanford CS 109 Textbook

  Bayesian Statistics

• A Student’s Guide to Bayesian Statistics

Mathematics for Machine Learning and Data Driven Problems

• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
• Mathematical Foundations for Data Analysis
• Mathematics for Machine Learning

Machine Learning

Introduction

• A Course in Machine Learning
• The StatQuest Illustrated Guide to Machine Learning

Statistical Perspective

• An Introduction to Statistical Learning

Probabilistic Perspective

• Probabilistic Machine Learning: An Introduction -
• Probabilistic Machine Learning: Advanced Topics - KTH DD2434 Textbook
• Probabilistic Programming and Bayesian Methods for Hackers

Variational Inference

All of these papers were not required for the KTH DD2434 class but it was the only way to survive the class.

• Variational Inference: A Review for Statisticians
• David Blei Variational Inference Notes
• Stochastic Variational Inference
• Blackbox Variational Inference
• Latent Dirichlet Allocation
• Variational Auto-Encoders

  Variational Inference Explainations

• A brief primer on variational inference
• Machine-Learning Variational Inference
• Variational Bayes and the Mean Field Approximation

Pattern Matching

• Pattern Recognition and Machine Learning - KTH DD2434 Textbook

Deep Learning

• Dive Into Deep Learning - KTH DD2424 suggested reading
• Understanding Deep Learning - KTH DD2424 suggested reading
• Deep Learning Foundations and Concepts - KTH DD2424 suggested reading

Applied Machine Learning

Computer Vision

• Foundations of Computer Vision

Natural Language Processing + Information Retrieval

• Speech and Language Processing - KTH DD2417 Textbook
• Introduction to Information Retrieval - KTH DD2477 Textbook

Visualization

• Visualization Analysis and Design
• The Grammar of Graphics
• Visualize This
• Data Sketches: A journey of imagination, exploration, and beautiful data visualizations
• The Functional Art
• The Truthful Art
• How Charts Lie
• The Art of Insight

Creative Coding

• The Nature of Code
• The Book of Shaders
• Code as a Creative Medium

Systems Programming

• Operating Systems: Three Easy Pieces
• Distributed Systems 4th Edition

Programming Language Theory

• How to Design Computer Programs
• Programming Languages: Application and Interpretation
• Professor Frisby’s Mostly Adequate Guide to Functional Programming

Engineering - Software + Systems

• The Pragmatic Programmer
• Designing Data-Intensive Applications

Engineering - Machine Learning + AI

• AI Engineering: Building Applications with Foundation Models
• Designing Machine Learning Systems
• Hands-On Large Language Models
• Build a Language Model from Scratch

Programming Languages + Tools

Tools

• Pro Git

Machine Learning

• Machine Learning with PyTorch and Scikit-Learn
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Systems Programming

Rust

• Rust Book

  Golang

• Go By Example
• Go With Tests

Web + The Internet

• Eloquent JavaScript

Data Analysis

R

• R For Data Science
• ggplot2: Elegant Graphics for Data Analysis

  Python

• Python For Data Analysis
• Python Data Science Handbook

Database Systems

• MySQL
• MariaDB
• Postgres
• SQLServer

Technical Inteviews

• Chip Huyen Machine Learning Interviews Book
• Yangshun - Tech Interview Handbook
• Neetcode