CENG 508 Advanced Artificial Intelligence
Course Syllabus
Instructor: Assist. Prof. Dr. Engin DEMIR Office: L-205 Office Hours: WED 13:30-15:20
Weekly Timetable:
FRI 18:00-21:00 (A-201 Balgat Campus)
Course Web Site: http://ceng508.cankaya.edu.tr & WebOnline
Course Description : A solid foundation in the principles and technologies that underlie many facets of AI, including logic, knowledge representation, probabilistic models, and machine learning. Additionally, students have the option to pursue particular topics in more depth, with coursework available in areas such as robotics, vision, natural language processing or machine learning.
Course Objectives :
- Learn logic and reasoning methods from a computational perspective
- Learn about agent, search, probabilistic models, perception and cognition, and machine learning
- Study state-of-the-art solutions to research problems
Learning Outcomes
- Compare AI with human intelligence and traditional information processing, and discuss its strengths and limitations and its application to complex and human-centered problems.
- Discuss the core concepts and algorithms of advanced AI, including informed searching, CSP, logic, uncertain knowledge and reasoning, dynamic Bayesian networks, graphical models, decision making, multiagent, inductive learning, statistical learning, reinforcement learning, natural language processing, robotics, and so on.
- Apply the basic principles, models, and algorithms of AI to recognize, model, and solve problems in the analysis and design of information systems.
- Analyze the structures and algorithms of a selection of techniques related to searching, reasoning, machine learning, and language processing.
- Design AI functions and components involved in intelligent systems, such as computer games, expert systems, semantic web, information retrieval, machine translation, mobile robots, decision support systems, and intelligent tutoring systems.
- Review research articles from well-known AI journals and conference proceedings regarding the theories and applications of AI.
References
- Artificial Intelligence: A Modern Approach, 3rd Edition. Russell & Norvig. 2010
- Broad background resource: AI Topics
Grading
| Midterm Exam | 20% |
| Final Exam | 30% |
| Project | 20% |
| Paper | 15% |
| Presentation | 15% |
Course Outline
|
Weeks |
Starting Dates |
Topics |
|
1 |
26/09/2016 |
Introduction and Brief History of AI |
|
2 |
03/10/2016 |
Intelligent Agents and Reasoning |
|
3 |
10/10/2016 |
Problem Solving by Searching |
|
4 |
17/10/2016 |
Constraint Satisfaction and Game Playing |
|
5 |
24/10/2016 |
Logical Agents and Knowledge Representation |
|
6 |
31/10/2016 |
Logical Reasoning Systems |
|
7 |
07/11/2016 |
Acting Logically: Planning |
|
8 |
14/11/2016 |
Uncertain Knowledge and Reasoning |
|
9 |
21/11/2016 |
Learning |
|
10 |
28/11/2016 |
Agents that communicate |
|
11 |
05/12/2016 |
Natural Language Processing |
|
12 |
12/12/2016 |
Perception |
|
13 |
19/12/2016 |
Student Presentations |
|
14 |
26/12/2016 |
Student Presentations |
|
15 |
02/01/2017 |
Final Exam Week |
Term Project:
17/10/2016 Project groups of at most 3 students will be announced.
24/10/2016 Groups will propose a project title with a brief description of the topic including a literature review.
21/11/2016 Design report is due.
19/12/2016 Final report is due.
Term Paper: (due 2/1/2017)
The students groups will write a manuscript on the project topic. Possible manuscript types are as follows:
- Comparative study: Groups will select at least 3 state-of-the-art solutions to compare and contrast the techniques in terms of efficiency and effectiveness.
- Experimental study: Groups will deploy a state-of-the-art solution to a new application area and elaborate on its performance in terms of efficiency and effectiveness.
- Novel study: Groups will propose a novel solution to a well-defined problem and justify their solution with either theoretical or experimental results.