Artificial Intelligence lecture notes. AI summarized notes for reading and may be for self-learning, I think.

1. ECEG-7601
Advanced Artificial Intelligence (AI)
Course Overview
Lecturer: Dr. Kinde A.

2. What is Intelligence?
• INTELLIGENCE
• The ability of a system to calculate, reason, perceive
relationships and analogies, learn from experience, store
and retrieve information from memory, solve problems,
comprehend complex ideas, use natural language fluently,
classify, generalize, and adapt new situations.

3. Types of Intelligence

4. What is Intelligence Composed of?

5. Components of Intelligence
• Reasoning − It is the set of processes that enables us to provide basis for
judgement, making decisions, and prediction.
• Learning − It is the activity of gaining knowledge or skill by studying,
practising, being taught, or experiencing something.
• Learning enhances the awareness of the subjects of the study.
• Problem Solving − It is the process in which one perceives and tries to arrive
at a desired solution from a present situation by taking some path, which is
blocked by known or unknown hurdles.
• Problem solving also includes decision making, which is the process of selecting the
best suitable alternative out of multiple alternatives to reach the desired goal are
available.
• Perception − It is the process of acquiring, interpreting, selecting, and
organizing sensory information.
• Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and
write the verbal and written language.

6. Artificial Intelligence (AI)
• John McCarthy, “ AI is the science and engineering of
making intelligent machines, especially intelligent computer
programs”.
• AI is a way of making a computer, a computer-controlled
robot, or a software think intelligently, in the similar manner
the intelligent humans think.
• AI is accomplished by studying how human brain thinks, and
how humans learn, decide, and work while trying to solve a
problem, and then using the outcomes of this study as a basis
of developing intelligent software and systems.

7. Difference between Human and
Machine Intelligence
• Humans perceive by patterns whereas the machines perceive
by set of rules and data.
• Humans store and recall information by patterns, machines
do it by searching algorithms.
• For example, the number 40404040 is easy to remember,
store, and recall as its pattern is simple.
• Humans can figure out the complete object even if some part
of it is missing or distorted; whereas the machines cannot do
it correctly.

8. Course Contents
1. Introduction to AI
2. Intelligent Agents
3. Problem Solving by Searching
4. Knowledge, Reasoning and Planning
5. Expert Systems
6. Uncertain Knowledge and Reasoning
7. Learning
8. Genetic Algorithms
9. Natural Language Processing
10. AI Programming using python

9. References
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice
Hall, 2010, 3rd edition
2. Patrick Henry Winston, Artificial Intelligence, 3rd Edition, AW, 1999.
3. Nils.J.Nilsson, Principles of Artificial Intelligence, Narosa Publishing House,
1992
4. Artificial Intelligence: A Systems Approach. M. Tim Jones, Infinity Science Press,
2008
5. Fausett, Laurene V.,(1994), Fundamentals of neural networks: architectures,
algorithms, and applications , New Jersey, Prentice-Hall, In
6. Nelson, Marilyn McCord & Illingworth, W.T., (1990), A practical guide to neural
networks , Massachusetts, Addison-Wesley Publishing Company, Inc.
7. Haykin, Simon, (1994), Neural networks , New Jersey, Macmillan Publishing
Company.
8. Freeman, James A. & Skapura, David M., (1991), Neural networks: algorithms,
applications, and programming techniques , USA, Addison-Wesley Publishing
Company, Inc.
9. Patterson, Dan W., (1996), Artificial neural networks: theory and applications ,
Singapore, Prentice Hall
10. Dan W. Patterson, “AI & Expert Systems”, Eastern, Economy Edition, 2000

10. Mark Distribution and Attendance Policy
Grading:
• Class participation (5%), Programming assignments and term
paper (30%), Midterm test (25%), Final exam (40 %)
• Class participation includes participation in both lectures and
tutorials (attendance, asking and answering questions).
Attendance policy:
• Attendance at every lecture, as per the university’s legislation