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EEC20401: Artificial Intelligence

Electrical and Communication Engineering

Course Overview
Programs:
  • BEng in Computer Engineering
  • BEng in Electronics and Communication Engineering
Level:4
Credit Hours:3 (2 hrs. Lecture, 2 hrs. Lab)
Prerequisite:Introduction to Computer Programming

Summary

This course introduces students to the fundamental concepts and techniques of artificial intelligence. This course involves implementing and applying key AI algorithms to solve a wide range of problems. Topics include problem solving, heuristic search, planning, game playing, reasoning with propositional and predicate logic, machine learning and applications.

Learning Outcomes
Upon completion of this course, students should be able to:
  1. 1.Explain the concepts of artificial intelligence
  2. 2.Implement various searching methods for solving problems
  3. 3.Design knowledge based agent to represent a complex real world environments
  4. 4.Implement appropriate learning algorithms to solve the real world problems
  5. 5.Apply artificial intelligent techniques to solve complex problems
  6. 6.Discuss classical and modern artificial intelligent applications
Syllabus

Introduction to Artificial Intelligence

  • Fundamentals
  • Applications
  • Intelligent Agents

Problem Solving

  • Solving Problems by Searching
  • Heuristic search techniques
  • Constraint satisfaction problems
  • Stochastic search methods

Game Playing

  • Minimax
  • Alpha-beta pruning

Knowledge and Reasoning

  • Building a Knowledge Base
  • Propositional logic
  • First order logic
  • Classical Planning
  • Knowledge representation
  • Uncertain Knowledge and Reasoning
  • Probabilities
  • Bayesian Networks

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised learning

Advanced Topics

  • Introduction to Natural Language Processing
  • Computer Vision
  • Robotics
Textbooks and Reading Materials

Main Textbook

Artificial Intelligence – A Modern Approach

Stuart Rusell & Peter Norvig (2020)

4th Edition - Pearson Education

ISBN: 978-0134610993

Additional Reading

  • The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications

    Kavita Ganesan (2018) - Opinosis Analytics Publishing

  • Artificial Intelligence & Generative AI for Beginners: The Complete Guide

    David M. Patel (2023) - Independently published

  • Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x

    Alberto Artasanchez, Prateek Joshi (2023) - Packt Publishing

Evaluation Methods

Coursework

Programming based Assignment / Lab exercises / Case study

50%

End term test

Unseen written exam, 1 ½ hours duration

50%

Pass Requirement:50%
Teaching Strategy

The main teaching method will be based on power point lectures, video lectures, E-learning, and practical sessions. Laboratory exercises are used to relate theoretical concepts to practical experience. The students will be expected to perform directed reading exercises and self-learning exercises on emerging technologies.