Introduction to Artificial Intelligence

Artificial Intelligence: A Modern Approach

Causality: Models, Reasoning and Inference

Dive into Deep Learning

Reinforcement Learning: An Introduction

Deep Learning

Introduction to Artificial Intelligence: Artificial Intelligence: A Modern Approach (3rd ed.) Chapt. 1 and 2

Uninformed Search: Artificial Intelligence: A Modern Approach (3rd ed.) Sect. 3.1-3.4

Informed Search: Artificial Intelligence: A Modern Approach (3rd ed.) Sec. 3.5, 3.6

Constraint Satisfaction: Artificial Intelligence: A Modern Approach (3rd ed.) Sec 6.1-6.3

Uncertainty: Artificial Intelligence: A Modern Approach (3rd ed.) Sect. 13.1-13.5

Bayesian Networks: Artificial Intelligence: A Modern Approach (3rd ed.) Sections 14.1, 14.2, 14.4

Causal Inference: Causality (2nd ed.) Chapter 1

Intro to ML and Decision Tree Learning: Artificial Intelligence: A Modern Approach (3rd ed.) Sec 18.1-18.4

Statistical Learning: Artificial Intelligence: A Modern Approach (3rd ed.) Sec 20.1-20.2

Neural Networks: Artificial Intelligence: A Modern Approach (3rd ed.) Sec 18.7, Dive into Deep Learning Chapter 5

Deep Neural Networks: Dive into Deep Learning Chapter 5

Sequence modeling, HMMs: Artificial Intelligence: A Modern Approach (3rd ed.) Sec 15.1-15.3, 15.5

RNNs, Attention and Transformers: Dive into Deep Learning Chapters 9, 11, Deep Learning Chapter 10

Markov Decision Processes: Artificial Intelligence: A Modern Approach (3rd ed.) Sections 17.1-17.4, Reinforcement Learning: An Introduction (2nd ed.) Chapter 3 and Sections 4.1-4.2, Dive into Deep Learning Sections 17.1-17.2

Intro to Reinforcement Learning: Artificial Intelligence: A Modern Approach (3rd ed.) Sections 21.1-21.3, Reinforcement Learning: An Introduction (2nd ed.) Sections 6.1-6.5, Dive into Deep Learning Section 17.3

Deep Reinforcement Learning: Reinforcement Learning: An Introduction (2nd ed.) Sections 9.4, 9.7, Deep Learning Chapters 6, 7, 8

Policy Gradient and Monte Carlo Tree Search: Reinforcement Learning: An Introduction (2nd ed.) Sections 8.11, 13.1-13.3

Model-based RL and RL from Human Feedback: Reinforcement Learning: An Introduction (2nd ed.) Sections 8.1-8.2 Ouyang, Wu, Jiang, Wainwright, et al. (2022) Training language models to follow instructions with human feedback, NeurIPS.

LLM Test-time Inference and Reasoning: Rafailov, Sharma, Mitchell, Ermon, Manning, Finn (2023) Direct Preference Optimization: Your Language Model is Secretly a Reward Model, NeurIPS. Rashid, Wu, Fan, Li, Kristiadi, Poupart (2025) Towards Cost-Effective Reward Guided Text Generation, ICML.

Game Theory: Artificial Intelligence: A Modern Approach (3rd ed.) Section 17.5, Reinforcement Learning: An Introduction (2nd ed.) Sections 21.1-21.3

Multi-agent RL: Caroline Claus and Craig Boutilier (1998) The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems, AAAI. Michael Littman (1994) Markov games as a framework for multi-agent reinforcement learning, Machine learning proceedings. Junling Hu and Michael P. Wellman (2003) Nash Q-learning for General-Sum Stochastic Games, JMLR

Image Generation: Steins (2022) Diffusion Models Clearly Explained Steins (2022) Stable Diffusion Clearly Explained Mohebbi, Abdulrahman, Miao, Poupart, Kothawade (2026) Image-POSER: Reflective RL for Multi-Expert Image Generation and Editing, arXiv.

Artificial Intelligence: A Modern Approach

Artificial Intelligence: Foundations of Computational Agents