اسم المدرس : معتز خالد حسين سعد

الكلية : كلية تكنولوجيا المعلومات

القسم : تكنولوجيا المعلومات

اسم المساق : الذكاء الاصطناعي

رقم المساق : CSCI 4304

وصف المساق :

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Artificial Intelligence Course

 

Artificial Intelligence Course Outline

Instructor: Motaz Saad
Course Name: Artificial Intelligence / Intelligent and Decision Support Systems
Course ID: CSCI4304 / SICT4402
Term: Spring 2019
Prerequisites: Programming, Data Structure.

Course Description

This course provides students with the main fundamentals of Artificial Intelligence (AI). The course covers the main techniques that are used in AI examples (from chess-playing to self-driving cars). These techniques include search algorithms, probability, reasoning and inference, programming logic, expert systems, rule-based systems, fuzzy logic, machine learning, knowledge representation, pattern recognition, and natural language processing. The course helps students to use AI to solve specific problems in their future careers. The theoretical part of the course focuses on understanding concepts, structures, and algorithms, while the practical part (lab) includes a set of exercises to be performed using AI tools such as CLIPS, Weka, and Matlab.

Textbooks

• Michael Negnevitsky, Artificial Intelligence: Intelligent Systems Approach, 3/E, ISBN: 9781408225745, 2011.
• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition 3/E, ISBN: 9781292153964, 2017.

Topics

• Introduction (1 week, chapter 01 from Intelligent Systems Approach book)
• Intelligent Agents (1 week, chapter 02 from Modern Approach book)
• Rule-based expert systems (1 week, Chapter 02 from Intelligent Systems Approach book)
• Problem Solving and Search Algorithms (2 weeks, chapter 03 and chapter04 from Modern Approach book)
  13. Problem Solving
  13. Search Algorithms
      18. Breadth-first search
      18. Uniform-cost search
      18. Depth-first search
      18. Depth-limited search
      18. Iterative deepening search
      18. Best-first search
      18. A* search
      18. Heuristics
• Game Playing (1 week, chapter06 from Modern Approach book). MinMax Algorithm.
• Fuzzy expert systems (1 week, Chapter 04 and Chapter 05 from Intelligent Systems Approach book)
• Artificial neural networks (Supervised) (Chapter 07 – Artificial Neural Networks – Supervised Learning)
• Artificial neural networks (Unsupervised) (Chapter 08 – Artificial Neural Networks – Unsupervised Learning)
• Evolutionary computation (Chapter 09 – Evolutionary Computation – Genetic Algorithms)
• Hybrid intelligent systems
  13. Chapter 11 – Hybrid Intelligent Systems – Neural Expert Systems and Neuro-fuzzy Systems
  13. Chapter 12 – Hybrid Intelligent Systems – Evolutionary Neural Networks and Fuzzy Evolutionary Systems
• Natural Language Processing (NLP Intro) https://youtu.be/l_v_EvUk_iU

Grading

• Lab 25%
• Mid exam 25%
• Final Exam 50%