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.
-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 Exam
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
Final Exam
16. Week
Final Exam
17. Week
Final Exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Homework / Term Projects / Presentations
1
30
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes 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.
LO-7
Gain experience of doing independent study and criticize research.
