Artificial Intelligence Course Specifications and Curriculum Syllabus

Course Specifications and Structural Overview

  • Course Title: Artificial Intelligence
  • Credit Hours: 3(2,1)3(2,1)
    • Lecture Credit Component: 22
    • Practical / Lab Credit Component: 11
    • Total Credit Hours: 33
  • Contact Hours: 2,32,3
    • Weekly Lecture Contact Hours: 22
    • Weekly Practical / Lab Contact Hours: 33
  • Pre-requisites: None

Course Learning Outcomes and Bloom Taxonomy Mapping

  • CLO-1: Understand the fundamental constructs of Python programming language.
    • Bloom Taxonomy Level: C2 (Understand)
  • CLO-2: Understand key concepts in the field of artificial intelligence.
    • Bloom Taxonomy Level: C2 (Understand)
  • CLO-3: Implement artificial intelligence techniques and case studies.
    • Bloom Taxonomy Level: C3 (Apply)

Course Foundations and Core Concepts

  • Significance of Artificial Intelligence: Artificial Intelligence has emerged as one of the most significant and promising areas of computing.
  • Foundational Focus: The course concentrates on the foundations of AI and its foundational operational techniques.
  • Core AI Techniques:
    • Symbolic manipulations
    • Pattern Matching
    • Knowledge Representation
    • Decision Making
  • Conceptual Distinctions: Appreciating and distinguishing the fundamental differences between:
    • Knowledge
    • Data
    • Code
  • Practical Tooling: Python programming language is designated for the practical implementation work of the course.

Detailed Curriculum Outline

  • Foundations and Knowledge-Based Systems:
    • Introduction to Artificial Intelligence
    • Applications toward Knowledge Based Systems
    • Introduction to Reasoning and Knowledge Representation
  • Problem Solving by Searching:
    • Uninformed searching strategies
    • Informed searching strategies
    • Heuristics design and evaluation
    • Local searching techniques
    • Game-playing algorithms:
    • Min-max algorithm
    • Alpha beta pruning
  • Benchmark Case Studies:
    • General Problem Solver
    • Eliza
    • Student
    • Macsyma
  • Learning Frameworks and Advanced Topics:
    • Learning from examples:
    • Artificial Neural Networks (ANN)
    • Natural Language Processing (NLP)
    • Recent trends in Artificial Intelligence
    • Practical applications of AI algorithms
  • Practical Application:
    • Utilization of the Python programming language to explore and illustrate various issues and techniques in Artificial Intelligence.

Bibliographic Reference List

  • Reference 1:
    • Authors: Russell, S. and Norvig, P.
    • Title: Artificial Intelligence: A Modern Approach
    • Edition: 3rd edition
    • Publisher: Prentice Hall, Inc.
    • Year: 2015
  • Reference 2:
    • Author: Norvig, P.
    • Title: Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
    • Publisher: Morgan Kaufman Publishers, Inc.
    • Year: 1992
  • Reference 3:
    • Authors: Luger, G.F. and Stubblefield, W.A.
    • Title: AI algorithms, data structures, and idioms in Prolog, Lisp, and Java
    • Publisher: Pearson Addison-Wesley
    • Year: 2009
  • Reference 4:
    • Author: Severance, C.R.
    • Title: Python for everybody: Exploring data using Python 3
    • Publisher: CreateSpace Independent Publ Platform
    • Year: 2016
  • Reference 5:
    • Author: Joshi, P.
    • Title: Artificial intelligence with python
    • Publisher: Packt Publishing Ltd.
    • Year: 2017
  • Reference 6:
    • Authors: Miller, B.N., Ranum, D.L. and Anderson, J.
    • Title: Python programming in context
    • Publisher: Jones & Bartlett Pub.
    • Year: 2019