/ai-csci4304

AI Course

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ai-csci4304

AI Course

https://mksaad.wordpress.com/2020/02/02/artificial-intelligence-course-spring-2020/

Instructor: Motaz Saad

Course Name: Artificial Intelligence / Intelligent and Decision Support Systems

Course ID: CSCI4304 / SICT4402

Term: Spring 2020

Prerequisites: Programming, Data Structure.

Artificial Intelligence Course Outline

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.
  • Alberto Artasanchez; Prateek Joshi, Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x and TensorFlow 2, 2nd Edition, ISBN 9781839219535, Publisher: Packt Publishing, Published: January 2020.

Topics

  • Introduction: What is AI?
  • State of the art of AI
  • Intelligent Agents (1 week, chapter2 from Modern Approach book)
  • Problem Solving and Search Algorithms (2 weeks, chapter3-and-4 from Modern Approach book)
  • Problem Solving
  • Search Algorithms
  • Breadth-first search
  • Uniform-cost search
  • Depth-first search
  • Depth-limited search
  • Iterative deepening search
  • Best-first search
  • A* search
  • Heuristics
  • Game Playing (1 week, chapter6 from Modern Approach book). MinMax Algorithm.
  • Rule-based expert systems (1 week, Chapter 02 from Intelligent Systems Approach book)
  • 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
  • Chapter 11 – Hybrid Intelligent Systems – Neural Expert Systems and Neuro-fuzzy Systems
  • Chapter 12 – Hybrid Intelligent Systems – Evolutionary Neural Networks and Fuzzy Evolutionary Systems
  • Natural Language Processing (NLP Intro) https://youtu.be/l_v_EvUk_iU