## Artificial Intelligence (CS 4793), Summer 2015

### University of Information Technology (UIT), Vietnam

#### July 6 - August 8, 2015

#### Lecture: MWF 8:30 - 11:30 AM, Room A106

#### Web site:

#### Instructor

- Dr. David Cline, Visiting Lecturer
- david.cline@okstate.edu
- office: A.1B3
- Office hours: 13:30PM - 16:30PM M-F

#### Teaching Assistants

- Hien Nguyen Dinh, Lecturer of Computer Science Faculty
- Van Ho Long, Lecturer of Computer Science Faculty

#### Text

- Artificial Intelligence: A Modern Approach, third edition. Stuart Russell and Peter Norvig.

#### Course Objectives

This class covers topics in Artificial Intelligence, including Search strategies, Reasoning with uncertain knowledge, Learning from data, Control systems, and Robotics. We will cover chapters 3, 4, 5, 10, 21, 25, 13, 14, and 18 of the AIMA text, in that order. Some of the topics we will cover are:

- Recursive search methods
- Advarsarial games
- Planning methods
- Reinforcement learning
- Simple control theory
- Probabilistic reasoning
- Example-based learning

#### Assignments

- Problem Sets
- There will be 4 problem sets throughout the term to help you cement your knowledge of the concepts and algorithms presented in class, and to help prepare you for the tests. These are indicated by (H) on the schedule.

- Code Projects
- During the semester there will be 5 coding assignments, two of which must be coded in Java, since you will be building on existing Java codebases. The other three may be written in the language of your choice. These are indicated by (P)

- Tests
- This class has a midterm and a final test. The midterm will cover chapters 3,4,5, and 10. It will be administered in class for the first two hours on 17/7/15. The final will be comprehensive and will be administered on 3/8/15.

#### Links to the assignments

- (P) Permutations
- (H) Homework 1: Chapter 3, key
- (P) Math Dice
- (H) Homework 2: Chapters 4,5,10
- (P) Sliding puzzle
- (H) Homework 3: Chapters 21,25
- (P) Pendulum, code: pendulum.zip (cancelled)
- (H) Homework 4: Chapters 13,14,18
- (P) Robot Localization, code: robot.zip

#### Homework Keys

#### Grade Breakdown

- Written Homework: 25%
- Programs: 35%
- Tests: 40%

#### Lecture Notes

- aiSlides.zip (after chapter 5 still needs revision)

#### Tentative Schedule

Date | Reading (AIMA) | Topics | Assignments |
---|---|---|---|

6/7/15 | Preliminaries, Agents, Recursion | ||

Permutations, Combinations | |||

3.1-3.4 | Search problems | ||

8/7/15 | 3.4 | Uninformed search | |

3.5 | A* search | ||

3.6 | Local search | (H) 3 | |

10/7/15 | 4.1-4.2 | Local search | |

4.3 | Nondeterminism | ||

4.4-4.5 | Online search | (P) Permutations, Combinations | |

13/7/15 | 5.1-5.2 | Adversarial games | |

5.3 | Heuristic functions | ||

5.4-5.6 | Math dice work session | (P) Math Dice | |

15/7/15 | 10.1 | Classical Planning | |

10.2 | POP and Planning Graphs | ||

Midterm Review | (H) 4,5,10 | ||

17/7/15 | (T) Midterm Exam | ||

Sliding puzzle work session | |||

20/7/15 | 21.1-21.3 | Reinforcement learning | |

21.4-21.6 | Active reinforcement learning | ||

25.1-25.3 | Robot perception | (P) Toroidal Sliding Puzzle | |

22/7/15 | 25.4-25.6 | Robot movement, controllers | |

18.1-18.6 | Learning from examples, Regression | ||

18.8-18.9 | Non-parametric models, Pendulum work session (afternoon) | (H) 21,25 | |

24/7/15 | 13.1-13.3 | Probability and inference | |

13.4-13.5 | Bayes’ rule | ||

14.1-14.3 | Bayesian Networks | (P) Pendulum | |

27/7/15 | 14.4-14.5 | Bayesian Learning | (H) 13, 18 |

18.7 | Neural Networks | ||

Final review | |||

29/7/15 | Practice final | ||

3/8/15 | (T) Final Exam | ||

3/9/15 | Robot work session | (P) Robot Localization |