Course Code:CSC412MC3 / CSC412SC3
Course Title:Artificial Intelligence
Academic Credits:03 (45 Hours of Lectures and Tutorials)
Objectives:To provide in-depth knowledge on design and analysis of intelligent systems for solving problems that are difficult or impractical to resolve using traditional approaches.
  • Formulate an efficient Intelligent system model for a problem expressed in natural language
  • Apply appropriate uninformed, informed or local search algorithms for solving problems
  • Devise a plan of action to achieve a goal using standard AI methods
  • Demonstrate the working of natural language processing techniques


  • Introduction: Practical examples of Artificial Intelligence, Intelligent Agents, Environments, Intelligent behaviour, Rational behaviour & Turing test
  • Problem solving by Searching: Problem-Solving Agents, Uninformed Search Strategies, Informed (Heuristic) Search Strategies
  • Local search and optimization algorithms: Hill climbing search, Simulated annealing, Local beam search, Genetic algorithms, searching in different environments, adversarial search
  • Planning: Classical planning, planning as state-space search
  • Learning Methodologies: Learning by Analysing Difference, by Recording Cases, by Correcting Mistakes, by Building Multiple models, by Building Identification Tree
  • Knowledge representation: Ontology engineering, Categories and objects, events
  • Natural Language Processing: Language models, Text classification, Information retrieval, Information extraction
Teaching Methods:Lecture by Lecturer, Recitation oral questions
Assessment/ Evaluation Details:
  • In-course Assessments ——————- 30%
  • End-of-course Examination ————- 70%