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The document provides an overview of an advanced artificial intelligence course, including definitions and components of intelligence, as well as AI's distinction from human intelligence. It outlines key topics such as intelligent agents, problem solving, reasoning, expert systems, and natural language processing, along with references for further study. Grading criteria include participation, assignments, tests, and exams, adhering to the university's attendance policies.
Lec 0 about the course
- 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.
- 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