- Course information:
Course Title: | Artificial Intelligence |
Hours | MW 3:00 – 4:15 |
Course Code: | ECE595 |
Credits: | 3 credits |
Term: | Fall 2016 |
Pre-requisite: | ECE 30200 and 35900 (or graduate standing) |
- Course Description
The course covers the application of Artificial Intelligence techniques and algorithms for problem solving. Students will learn to apply different machine learning approaches to solve real life problems related to searching, parsing, identification, prediction, clustering, feature selection, etc… The concepts of reasoning an inference will be discussed and used as an approach to reason about difficult problems and derive suitable algorithms for these problems.
Students are expected to discuss, critique and extend current AI research papers as well as implement the algorithms discussed in class using their programming language of choice.
- Learning Objectives
Upon successful completion of the course, students should be able to
- design and implement deterministic, heuristic and optimization algorithms
- represent and analyse knowledge
- use inference and reasoning to solve real-life problems
- develop and implement machine learning approaches
- apply machine learning approaches to different applications and contexts.
- Textbook & Resources
Artificial Intelligence: A Modern Approach, Third Edition, Stuart Russell & Peter Norvig, Pearson Education Inc., December, 2009 ISBN: 978-0-13-604259-4
- Assessment Policy
There will be two midterms and a final exam. Students will also complete a project that will be assigned in the form of sub-projects throughout the semester.
Mid-term (2): 40 %
Project (s): 35%
Final-exam: 25 %
- General Classroom and Course Policies
Cheating on exams and projects will not be tolerated! Duplication and/or usage of any part of another student’s exam/homework is considered to be plagiarism. The minimum penalty assessed to a student cheating or plagiarizing (or aiding another student to do so) will be a grade of zero on the project or exam. The instructor reserves the right to administer further penalties (a failing course grade, notification of the administration, etc…).
You are to take exams on the times they are scheduled.
Projects are divided into tasks with due date as indicated below. Each task will be graded independently and must include a report and properly commented code when applicable.
- Course Topics
Date | Topic | Reading | Task Due Date |
|
1. | 8/23 | Introduction | Chapter 1 | |
2. | 8/25 | Agents | Chapter 2 | |
3. | 8/30 | Solving Problems by Searching (part I) | Chapter 3 | |
4. | 9/1 | Solving Problems by Searching (part 2) | Chapter 3 | Task 1 |
5. | 9/6 | Beyond Classical Search (Informed Search) | Chapter 4 | |
6. | 9/8 | Beyond Classical Search (Local Search) | Chapter 4 | |
7. | 9/13 | Game Playing | Chapter 5 | Task 2 |
8. | 9/15 | Paper 1 presentation | ||
9. | 9/20 | Constraint Satisfaction | Chapter 6 | |
10. | 9/22 | Review & QA | ||
11. | 9/27 | Midterm #1 | ||
12. | 9/29 | Logical Agents (part I) | Chapter 7 | |
13. | 10/4 | Logical Agents (part II) | Chapter 7 | Task 3 |
14. | 10/6 | Paper 2 presentation | ||
15. | 10/11 | First Order Logic | Chapter 8 | |
16. | 10/13 | Inference in First Order Logic | Chapter 9 | Task 4 |
10/18 | Spring Break – holiday | |||
17. | 10/20 | Uncertainty | Chapter 13 | |
18. | 10/25 | Paper 3 presentation | ||
19. | 10/27 | Bayesian Networks | Chapter 14 | |
20. | 11/1 | Inference in Bayesian networks | Chapter 15 | Task 5 |
21. | 11/3 | Review & QA | ||
22. | 11/8 | Midterm #2 | ||
23. | 11/10 | Speech Recognition | Chapter 15 | |
24. | 11/15 | Paper 4 presentation | ||
25. | 11/17 | Simple Decisions | Chapter 16 | Task 6 |
26. | 11/22 | Learning from Observations | Chapter 18 | |
11/24 | Thanksgiving – holiday | |||
27. | 11/29 | Neural Networks | Chapter 18 | |
28. | 12/1 | Statistical learning | Chapter 20 | Task 7 |
29. | 12/6 | Natural Language Processing | Chapter 22 | |
30. | 12/8 | Review & QA | ||
12/15 | Final Exam 3:30pm to 5:30pm |