Research Seminar: Documentation Using LaTeX

Research Project

Mobile Computing 2019

This course provides a broad introduction to Wireless Communication systems, the properties of and impairments in wireless communication systems, wireless communication system architectures and their implementations issues, mobility of wireless devices, mobile communication systems, their evolution, ad hoc networks and the latest advancements in wireless communication systems. 


Data Mining and Machine Learning

Advanced Algorithm Design

System Analysis and Design

High Performance Computing (2018)

Artificial Intelligence

Course Code:CSC412MC3
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.
ILOs:
  • 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

 

Contents:
  • 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 optimisation 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%

Compiler Design