My (Serious) Reading List to Learn AI/Data Science in 2024

Scientific Computing

1. Python for Data Analysis

2. R for Data Science

3. Computing with Data

Stats/Math

1. Stanford CS109

2. Harvard STAT110

3. Practical Stats for Data Scientists by Bruce & Bruce

4. Think Stats by Allen Downey

5. Think Bayes by Allen Downey

6. Statistical Rethinking by Richard McElreath: Advanced course in Bayesian data analysis, causality, and statistical modelling. It is taught by a non-mathematician in an intuitive way, explaining advanced and interdependent topics with a lot of analogies. The author offers the course in two formats, a book and lectures.

7. Causal inference in statistics By Judea Pearl

8. All of Statistics: Comprehensive reference

9. Mathematics for Machine Learning: Assuming you have studied college calculus and algebra, this book is a great compilation for the math you need for ML

10. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

11. Bayesian Data Analysis course at Aalto (CS-E5710) by Aki Vehtari: Advanced material

Machine Learning

1. Andrew Ng’s ML Specialization on Coursera: Your baby steps in ML

2. An Introduction to Statistical Learning: Essential ML book. Full of examples and applications.

3. Stanford CS229: Good lectures on the theoretical ML

4. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Practical approach to learn ML. Good after having the theory.

5. The Elements of Statistical Learning: Advanced material

Deep Learning

1. Understanding Deep Learning: I’m lucky to be in 2024 and read this book! It is a good shelter in the concurrent deep learning storms!

2. PyTorch course by Daniel Bourke

3. Andrej Karpathy Tutorials

NLP/LLMs

1. Introduction to Information Retrieval: Good starting point in NLP. This classic is authored by Christopher Manning et al.

2. CMU Advanced NLP 2024 by Graham Neubig: Lectures are on YouTube and exercises on Github

3. Hugging Face’s NLP Course

4. Andrej Karpathy Tutorials for building/hacking LLMs from scratch!

5. Speech and Language Processing by Jurafsky: Comprehensive theoretical reference

Computer Vision

1. Stanford CS231

2. Deep Learning for Vision Systems by Mohamed Elgendy

Reinforcement Learning

1. Reinforcement Learning: An Introduction by Sutton and Barto: Essential textbook. Can be used as a theoretical reference

2. RL Course 2015 by David Silver by DeepMind x UCL

3. Reinforcement learning by Phil Winder: supplemental material contains helpful code. Can be a good starting point, balanced between theory and practice.

4. Deep Reinforcement Learning (CS 285) at UC Berkeley: Intensive course by a top voice in RL (lectures available on YouTube)

5. Spinning up by OpenAI

MLOps - SWE for Data Intensive Apps

1. Designing Data-Intensive Applications

2. Made With ML

3. Full Stack Deep Learning

Misc.

• The Data Science Design Manual by Skiena

• Veridical Data Science

• Storytelling with Data: A Data Visualization Guide for Business Professionals

• Storytelling with Data: Let’s Practice

• MIT 14.310x Data Analysis for Social Scientists