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Ai Course Outline

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COURSE OUTLINE

GIFT SCHOOL OF COMPUTING SCIENCE

CS-415

Artificial Intelligence

B.S Computer Science

Fall Semester, 2018

Faculty:

GIFT School of Computing Science

Credit hours:

3 

Course level:

Undergraduate

Campus/Location/Instruction Mode:

GIFT University/On Campus/In Person

Course Convenor:

Qamar Askari

Consultation hours:

TBD

Pre-requisite:

Analysis of Algorithm

Timing

Please see the timetable

This document was last updated:

27th Oct, 2018

BRIEF COURSE DESCRIPTION

The goal of this course is to introduce the students to the field of AI and various search problems and problem solving methods used in AI.  In this course they will solve various problems using AI algorithms.  At the end of the course it is expected that the students will appreciate the importance of AI in different domains and be able to understand, design and to an extent implement some basic AI systems.


COURSE AIMS AND LEARNING OUTCOMES

After completing this course student should be able to:

  • Understand and build intelligent agents.
  • Solve search problems by picking up an appropriate algorithm or by designing their own solution.
  • Solve optimization problems using approximate algorithms.
  • Make their programs able to learn and improve with time.
  • Represent knowledge in appropriate form and reason on that knowledge using a specific language.
  • Learn about some successful applications of artificial intelligence.

Topic

Recourse

Week

Introduction to AI

  • Definition
  • Intelligent Agents and environments
  • Application areas and applications

Chap 1,2 & handout/internet reference

Week – 1

Problem solving, Games & Searching

  • Problem formulation & example problems
  • Uninformed and informed searches
  • BFS, DFS, DLS, ID and bidirectional search
  • BFS, Branch and bound, A* search
  • Local & global search
  • Hill climbing, beam search, simulated annealing
  • Adversarial search
  • Minimax search,  α-β pruning, games with element of chance, 3 player game

Chap 3,4 & 5

Week 2-5

Optimization & Metaheuristics

  • Introduction and example problems
  • Exact algorithms
  • Approximate algorithms/Metaheuristics
  • Genetic Algorithm
  • Introduction to PSO/ACO etc.

Extra resource

Week 6-7

Midterm

Week 8

Learning

  • Supervised learning
  • Regression/Neural networks
  • Unsupervised learning
  • K-means algorithm
  • Introduction to reinforcement learning

Extra Resource

Week 8-10

Knowledge representation, logic & reasoning

  • Types of knowledge
  • Knowledge representations
  • Propositional logic
  • First order logic
  • Reasoning
  • Expert Systems
  • Prolog
  • Uncertain reasoning
  • Probability basics
  • Naïve Bayes rule and its use
  • Statistical learning using Naïve Bayes

Chap 7,8, 9 and 13

Week 10-13

Application areas

  • Computer vision
  • Segmentation & recognition
  • Information retrieval
  • Clustering and classification
  • Gaming
  • Move planning etc.
  • Robotics
  • Motion & path planning

A brief introduction to above areas and application of AI in above areas will be talked about.

Extra Resource

Week 14

Project evaluation

Week 15-16

TEXTBOOK

  • Artificial Intelligence - A Modern Approach by Stuart Russel and Peter Norvig, Prentice Hall, Third Edition, 2010.

PROJECT

This is a very important part of this course which can be observed from weightage assigned to project. In very first week students will be given an assignment which will help them to decide a project by the end of 3rd week. In addition to giving you a way to learn how AI techniques are applied at larger scale the project could also be extendible to FYP in next semester.

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