# Probabilistic Foundations of Artificial Intelligence

## Overview

How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit ''intelligent'' behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students.## Topics covered

- Search (BFS, DFS, A*), constraint satisfaction and optimization
- Tutorial in logic (propositional, first-order)
- Probability
- Bayesian Networks (models, exact and approximate inference, learning)
- Temporal models (Hidden Markov Models, Dynamic Bayesian Networks)
- Probabilistic planning (MDPs, POMDPs)
- Reinforcement learning
- Combining logic and probability

## News

- Homework solutions have been posted.
- Consider taking Prof. Marcus Hutter's course on Foundations of Artificial Intelligence (VVZ entry).
- Additional lecture in CAB G51 at 8:15 on 6.12.2011
- No classes on 16.12. and 23.12.

## Details

**VVZ Information:**See here.**Lecture:**Friday 10-12 in CAB G 56**Recitations:**Friday 13-14 in CAB G 56**Teaching assistants:**Hastagiri Vanchinathan [hastagiri (at) inf (dot) ethz (dot) ch] and Yuxin Chen [yuxin (dot) chen (at) inf (dot) ethz (dot) ch]**Textbook:**S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (3rd Edition).

## Homeworks

- Homework 1 [pdf] Solutions 1 [pdf]
- Homework 2 [pdf] Solutions 2 [pdf]
- Homework 3 [pdf] Solutions 3 [pdf]
- Homework 4 [pdf] Solutions 4 [pdf]
- Homework 5 [pdf] Solutions 5 [pdf]
- Homework 6 [pdf]. Data files: trainingData.txt and testingData.txt. Solutions 6 [pdf]

## Solutions

- TBA

## Lecture Notes

- Sept 23: Introduction, Uninformed search (Chapters 1 and 2)
- Sept 30: Uninformed and informed search (Chapters 3.1-3.6)
- Oct 7: A primer in Logic (Chapters 7.1-8.3)
- Oct 14: First order logic; Intro to probability (Chapters 9.1-9.2, 9.5, 13.1-5)
- Oct 21: Bayesian Networks (Chapters 14.1-14.2); JavaBayes applet
- Oct 28: Inference in Bayesian Networks (Chapters 14.4)
- Nov 4: Approximate Inference; Information Gathering (Chapter 14.5; Bishop Chapter 8.4)
- Nov 11: Temporal models: HMMs, Kalman Filters, DBNs (Chapter 15.1-15.5)
- Nov 18: Probabilistic Planning: MDPs (Chapter 17.1-17.3). Value iteration demo (at UBC)
- Nov 25: POMDPs. Supervised Learning: Regression and Classification (Chapter 18.1-2, 18.6; Bishop Chapter 1.1, 3.1.). Linear regression demo (at UIUC). Logistic regression demo (at Technion).
- Dec 2: Learning Bayesian Networks (Chapter 20.1-2).
- Dec 6: Reinforcement learning (Chapter 21.1-3). Q-learning demo (at Northwestern).
- Dec 9: Logic and probability (Chapter 14.6). Alchemy (University of Washington).

## Relevant Readings

- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 (optional)