Textbooks

These are textbooks/papers that I find really helpful and consult frequently.

Table of Contents

1. Textbooks
   1. Table of Contents
   2. 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
   3. Data Tools - Programming Languages, Libraries, Frameworks
      1. Programming Languages
      2. Numerical Computing Tools
      3. Machine Learning/Deep Learning
      4. Databases
   4. Engineering
   5. Systems - AIML, Distributed
   6. Machine Learning
      1. Introduction
      2. Statistical Perspective
      3. Applied (Modern + Generative Modeling)
      4. Probabilistic Perspective
      5. Deep Generative Modeling
      6. Variational Inference
         1. Papers
         2. Deep Dive Explanations
      7. Pattern Matching
      8. Deep Learning
      9. Reinforcement Learning
   7. Applied Machine Learning
      1. Computer Vision/Image Analysis
      2. Audio
      3. Robotics
      4. Visualization
      5. Creative Coding
      6. Language Technologies
   8. Technical Interviews

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Mathematics

Calculus

• Calculus by James Stewart - any edition works

Linear Algebra

• Linear Algebra and Its Applications
• Introduction to Linear Algebra
• Think 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
• Kalman and Bayesian Filters in Python

Data Tools - Programming Languages, Libraries, Frameworks

Programming Languages

• Eloquent JavaScript

Numerical Computing Tools

• Python For Data Analysis
• Python Data Science Handbook

Machine Learning/Deep Learning

• Machine Learning with PyTorch and Scikit-Learn

Databases

• MySQL
• MariaDB
• Postgres
• SQLServer

Engineering

• The Pragmatic Programmer
• Pro Git

Systems - AIML, Distributed

• AI Engineering: Building Applications with Foundation Models
• Designing Machine Learning Systems
• Designing Data-Intensive Applications
• Building Machine Learning Systems with a Feature Store - KTH ID2223 Textbook
• How to Scale Your Model

Machine Learning

Introduction

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

Statistical Perspective

• An Introduction to Statistical Learning

Applied (Modern + Generative Modeling)

• Volodomyr Kuleshov Notes

Probabilistic Perspective

• Probabilistic Machine Learning: An Introduction - KTH DD2447 Textbook
• Probabilistic Machine Learning: Advanced Topics - KTH DD2434 Textbook, KTH DD2447 Textbook

Deep Generative Modeling

• The Principles of Diffusion Models
• Deep Generative Modeling

Variational Inference

Variational Inference is an approximation method in Probabilistic Machine Learning and was the focus of KTH DD2434. It’s very confusing if you are unfamiliar with some of the traditional methods like MCMC, SMC and Bayesian Statistics(this was me when I took the class lol)

Papers

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

Deep Dive Explanations

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

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
• Hands-On Machine Learning with Scikit-Learn and PyTorch - KTH ID2223 textbook
• The StatQuest Illustrated Guide to Neural Networks and AI

Reinforcement Learning

• Reinforcement Learning: Second Edition

Applied Machine Learning

Computer Vision/Image Analysis

• Foundations of Computer Vision - Berkeley CS 180 textbook
• Computer Vision Algorithms and Applications - KTH DD2423, Berkeley CS 180 textbook
• Digital Image Processing - KTH DD2423 textbook

Audio

• Digital Signals Theory
• PCP Notebooks. If interested in music processing check out these notebooks before the PCPT Notebooks and then tackle the FMP notebooks.
• PCPT: A Preparation Course in PyTorch. Through the Principles of Signal Processing - another groundbreaking book by the legendary Meinard Mueller. Book Website(Free Download). If interested in music processing highly recommend Mueller’s materials because they teach advanced methods in the context of digital signals and music.
• Fundamentals of Music Processing - KTH DT2470 textbook. Download available through University Library(ie. KTH Biblioteket).
• Introduction to Speech Processing. Open source book and materials from Aalto University which is particularly famous for audio and acoustics research.

• George Tzanetakis - A Music and Sound Exposition of the DFT

• Music Similarity and Retrieval - An Introduction to Audio-and-Web-based Strategies - Download available through University Library(ie. KTH Biblioteket).

Robotics

• Python Robotics

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

Language Technologies

• Natural Language Processing with Python - KTH DD2417 reference textbook
• Speech and Language Processing - KTH DD2417 textbook
• Introduction to Information Retrieval - KTH DD2477 textbook

Technical Interviews

• Chip Huyen Machine Learning Interviews Book
• 100 Numpy Exercises
• Neetcode
• Silvia Sapora ML Interviewing