OHJ-2556 Artificial Intelligence, 6 cr

Prof. Tapio Elomaa

Spring 2012, Jan. 10 - May, 10

Lectures: Tue 12-14 TB219 and Thu 12-14 TB223

Exercise group: M.Sc. Teemu Heinimäki Tue 14-14 TC133 and Wed 14-16 TB223

Course exam: Mon May 21, 2012, 9-12

News

The course exam has moved to Mon May 21, 9-12 AM
Exceptions to lectures:
March 5 - 11 Period Break
Apr. 5-11 Easter Break
Travel Apr. 18-20
May 1 VAPPU!

General Information

A course on programming (e.g., OHJ-1156 Programming II) is an obligatory prerequisite.
Courses OHJ-2156 Analysis of Algorithms and OHJ-2306 Introduction to Theoretical Computer Science help to follow the lectures and are, thus, recommended prerequisites.
The components of the course are:
- Lectures (attend!, optional)
- Weekly exercises (attend!, yields max 6 extra points)
- Essay (compulsory, max 10 points), Deadline in May
- Presentation on the essay topic (optional, yields max 4 extra points)
- Exam (compulsory)

Lectures

The lectures are based on the textbook:

Russell & Norvig: Artificial Intelligence: A Modern Approach, Third ed., Pearson, 2010.

Week	Dates	Slide Numbers	Chapters in the Course Book
1	Jan. 10 and 12	1 - 36	1 Introduction, 2 Intelligent Agents
2	Jan. 17 and 19	37 - 65	3 Solving Problems by Searching
3	Jan. 24 and 26	66 - 101	4 Beyond Classical Search, 5 Adversarial Search
4	Jan. 31 and Feb. 2	103 - 134	7 Logical Agents
5	Feb. 7 and 9	135 - 164	7, 8 First-Order Logic, 9 Inference in First-Order Logic

Weekly Exercises

There are two exercise sessions per week. They are concerned with topics that were lectured the previous week.
Questions to next week's exercises are published on this page on previous Wednesday the latest.

You should answer the questions by yourself in advance.
The "correct" answer is checked at the excercise session.
Be prepared to tell your answer to the other students.

Exercise	Problems
1	Jan. 24-25, 2012
2	Jan. 31-Feb. 1, 2012
3	Feb. 7-8, 2012
4	Feb. 14-15, 2012

Home Work

TBA

Topics

We intend to cover the following chapters from the textbook:

Lecture week Topic

1 I Artificial Intelligence

1 Introduction

2 Intelligent Agents

2-4 II Problem-solving

3 Solving Problems by Searching

4 Beyond Classical Search

5 Adversarial Search

6 Constraint Satisfaction Problems

5-7 III Knowledge, reasoning, and planning

7 Logical Agents

8 First-Order Logic

9 Inference in First-Order Logic

10 Classical Planning

11 Planning and Acting in the Real World

8-10 IV Uncertain knowledge and reasoning

13 Quantifying Uncertainty

14 Probabilistic Reasoning

16 Making Simple Decisions

17 Making Complex Decisions

11-14 V Learning

18 Learning from Examples

19 Knowledge in Learning

20 Learning Probabilistic Models

21 Reinforcement Learning

Lecture week	Topic
1	I Artificial Intelligence
	1 Introduction
	2 Intelligent Agents
2-4	II Problem-solving
	3 Solving Problems by Searching
	4 Beyond Classical Search
	5 Adversarial Search
	6 Constraint Satisfaction Problems
5-7	III Knowledge, reasoning, and planning
	7 Logical Agents
	8 First-Order Logic
	9 Inference in First-Order Logic
	10 Classical Planning
	11 Planning and Acting in the Real World
8-10	IV Uncertain knowledge and reasoning
	13 Quantifying Uncertainty
	14 Probabilistic Reasoning
	16 Making Simple Decisions
	17 Making Complex Decisions
11-14	V Learning
	18 Learning from Examples
	19 Knowledge in Learning
	20 Learning Probabilistic Models
	21 Reinforcement Learning

Course Grading

Literature

Stuart Russell & Peter Norvig: Artificial Intelligence: A Modern Approach, Third ed., Pearson, 2010