This course is part of the B.E. Computer Science Engineering curriculum under Anna University Regulation 2021. The knowledge from this course continues to be actively applied in professional software development.
Semester 4 (Second Year)
4 Credits
60 Lecture Hours
Course Overview
UniversityAnna University
Regulation2021
Semester4
Credits4
TypeCore
Units5
Course Objectives
1
To understand AI problem-solving techniques
2
To learn probabilistic reasoning and Bayesian networks
3
To understand supervised learning algorithms
4
To learn ensemble techniques and unsupervised learning
5
To implement neural networks and deep learning
Syllabus
Detailed unit-wise breakdown of the course curriculum as per Anna University Regulation 2021.
1
PROBLEM SOLVING
12 Hours
Introduction to AI and applicationsProblem solving agentsSearch algorithmsUninformed search strategiesHeuristic search strategiesLocal search and optimizationAdversarial searchConstraint satisfaction problems (CSP)
2
PROBABILISTIC REASONING
12 Hours
Acting under uncertaintyBayesian inferenceNaïve Bayes modelsProbabilistic reasoningBayesian networksExact inference in BNApproximate inference in BNCausal networks
3
SUPERVISED LEARNING
12 Hours
Introduction to machine learningLinear Regression – Least squaresSingle & multiple variablesBayesian linear regressionGradient descentLinear Classification – Discriminant functionLogistic regressionNaive Bayes classifierSupport vector machineDecision TreeRandom forests
PerceptronMultilayer perceptronActivation functionsNetwork training – Gradient descent optimizationStochastic gradient descentError backpropagationFrom shallow to deep networksVanishing gradient problemReLUHyperparameter tuningBatch normalizationRegularization and dropout
Course Outcomes
Upon completion of this course, students will be able to:
CO1
Apply AI search algorithms for problem solving
CO2
Use probabilistic reasoning for uncertainty handling
CO3
Implement supervised learning algorithms
CO4
Apply ensemble and unsupervised learning techniques
CO5
Build and train neural networks
Industry Application & Relevance
How the concepts learned in this course are applied in real-world software development projects across Banking, Healthcare, and Enterprise domains over 20+ years of experience.
Professional Application
AI integration, intelligent systems, predictive analytics
Textbooks & References
Textbooks
Stuart Russell, Peter Norvig, 'Artificial Intelligence: A Modern Approach', Pearson
Ethem Alpaydin, 'Introduction to Machine Learning', MIT Press
Reference Books
Tom Mitchell, 'Machine Learning', McGraw Hill
Christopher Bishop, 'Pattern Recognition and Machine Learning', Springer
Related Courses from Semester 4
Other courses from the same semester that are actively used in professional work.