This course covers both the basic theory and practical applications of AI which is the science and engineering of producing intelligent agents that can behave rationally. It aims to introduce the undergraduates with the computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. It emphasizes the techniques outlined as combinatorial search, probabilistic models and reasoning.
Prerequisite(s)
None
Corequisite(s)
None
Special Requisite(s)
None
Instructor(s)
Assist. Prof. Dr. Fatma Patlar Akbulut
Course Assistant(s)
-
Schedule
Not offered.
Office Hour(s)
Not offered.
Teaching Methods and Techniques
-Oral and Written presentations and applications
Principle Sources
-Russell and Norvig, Artificial Intelligence, A Modern Approach, 3rd Edition
-Lecture Slides
Other Sources
-See also http://aima.cs.berkeley.edu/ for additional resources including
Code http://aima.cs.berkeley.edu/code.html
Demos http://aima.cs.berkeley.edu/demos.html
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to Artificial Intelligence and Intelligence Agents
2. Week
Solving Problems by searching and Informed search
3. Week
Solving Problems by searching and Informed search
4. Week
Constraint Satisfaction and Adversarial Search
5. Week
Constraint Satisfaction and Adversarial Search
6. Week
Logical Agents
7. Week
First Order Logic
8. Week
Midterm
9. Week
Inference in First Order Logic
10. Week
Knowledge Representation
11. Week
Quantifying Uncertainity
12. Week
Probabilistic Reasoning
13. Week
Learning From Examples and Decision Trees
14. Week
Reinforcement Learning
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Homework / Term Projects / Presentations
1
30
Final Exam
1
40
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Understand the main approaches to artifical intelligence such as heuristic search, game search, logical inference, statistical inference, decision theory, planning, machine learning, neural networks and natural language proccessing
LO-2
Recognize problems that may be solved using artificial intelligence
LO-3
Implement artificial intelligence algorithms for hands-on experiences
LO-4
Explain recent developments in interdisciplinary fields on brain modeling and understanding how to work and assess brain inspired learning algorithms
LO-5
Have some experience gained with homeworks and one term project.
LO-6
Gain experience of doing independent study and criticize research.
