### 인공지능 (Artificial Intelligence)

- 강의실: 공학 3관 xxx호
- Textbooks
- D. L. Poole, A. K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, (2nd edition), Cambridge University Press, 2017 [book][pdf]
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Third edition), Prentice Hall, 2010 [book]

- Textbooks (additional)
- I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, 2016 [book]
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998 [link] [2nd_edition]
- D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 [book]

- References
- TensorFlow [link]
- Tensorflow Lecture at Stanford U. [link]
- AI lecture at Brown Univ.
- AI lecture at Berkeley Univ. [link]
- AIML lecture at Princeton Univ. [link]
- Deep RL lecture at Berkeley Univ. [link]
- Deep RL Tutorial at ICML '17 [link]

- Chapter 1: Aritifial intelligence and agents [pdf]
- Chapter 2: Agent architectures and hiearchical control [pdf]
- Chapter 3: Search for solutions [pdf]
- Chapter 4: Reasoning with constraints [pdf] [pdf]
- Chapter 5: Propositions and inference [pdf] [pdf]
- G. Antoniou. Tutorial on default logics, 1999 [pdf]

- Chapter 6: Planning with certainty [pdf] [pdf]
- B. Bonet, H. Geffner, Planning as heuristic search, AI '01 [pdf]
- J. Hoffmann, B. Nebel, The FF Planning Systems, JAIR '01 [pdf]
- S. Richter and M. Westphal. The LAMA Planner. 2010 [pdf]
- N. Lipovetzky and H. Geffner. A Polynomial Planning Algorithm that Beats LAMA and FF. 2017 [pdf]
- [ref] ICAPS Competitions [link]

- Chapter 7: Supervised machine learning [pdf] [pdf] [pdf]
- Decision tree [pdf]
- Intro to machine learning [pdf]
- Linear regression [pdf]
- Linear classification [pdf]
- Logistic regression [pdf]
- Neural networks I [pdf]
- Neural networks II [pdf]
- [Ref] The Matrix Calculus You Need For Deep Learning [pdf]
- [Ref] Matrix Calculus [pdf]

- Chapter 8: Reasoning with uncertainty [pdf]
- Chapter 9: Planning with uncertainty [pdf]
- Markov decision process [pdf]
- Planning by dynamic programming [pdf]

- Chapter 10: Learning with uncertainty [pdf]
- Chapter 11: Multiagent systems [pdf]
- Chapter 12: Learning to act (reinforcement learning) [pdf]
- [Toronto] Reinforcement learning [pdf]
- Model-free prediction [pdf]
- Model-free control [pdf]
- Value function approximation [pdf]
- Policy gradient [pdf]

- Chapter 13: Individuals and relations [pdf]
- Chapter 14: Ontologies and knowledge-based systems [pdf]
- Chapter 15: Relational planning, learning, and probabilistic reasoning [pdf]

- Assignment 1 [pdf]
- Assignment 2 [pdf]
- Assignment 3 [pdf]
- Assignment 4 [pdf]
- Assignment 5 [pdf]
- Assignment 6 [pdf]