CS3491: Artificial Intelligence and Machine Learning syllabus for MEE 2021 regulation

Artificial Intelligence and Machine Learning detailed syllabus for Medical Electronics (MEE) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the MEE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Medical Electronics 6th Sem scheme and its subjects, do visit MEE 6th Sem 2021 regulation scheme. The detailed syllabus of artificial intelligence and machine learning is as follows.

Course Objectives:

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Unit I

PROBLEM SOLVING
Introduction to AI – AI Applications – Problem solving agents – search algorithms – uninformed search strategies – Heuristic search strategies – Local search and optimization problems -adversarial search – constraint satisfaction problems (CSP)

Unit II

PROBABILISTIC REASONING
Acting under uncertainty – Bayesian inference – naive bayes models. Probabilistic reasoning -Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.

Unit III

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Unit IV

ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning – bagging, boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization

Unit V

NEURAL NETWORKS
Perceptron – Multilayer perceptron, activation functions, network training – gradient descent optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep networks -Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch normalization, regularization, dropout.

Practical Exercises

1. Implementation of Uninformed search algorithms (BFS, DFS)
2. Implementation of Informed search algorithms (A*, memory-bounded A*)
3. Implement naive Bayes models
4. Implement Bayesian Networks
5. Build Regression models
6. Build decision trees and random forests
7. Build SVM models
8. Implement ensembling techniques
9. Implement clustering algorithms
10. Implement EM for Bayesian networks
11. Build simple NN models
12. Build deep learning NN models

Course Outcomes:

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Text Books:

1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, Fourth Edition, Pearson Education, 2021.
2. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.

Reference Books:

1. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education,2007
2. Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligence”, McGraw Hill, 2008
3. Patrick H. Winston, “Artificial Intelligence”, Third Edition, Pearson Education, 2006
4. Deepak Khemani, “Artificial Intelligence”, Tata McGraw Hill Education, 2013 (http://nptel.ac.in/)
5. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
6. Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition,1997.
7. Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014
8. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, “Foundations of Machine Learning”, MIT Press, 2012.
9. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016

For detailed syllabus of all other subjects of Medical Electronics, 2021 regulation curriculum do visit MEE 6th Sem subject syllabuses for 2021 regulation.

For all Medical Electronics results, visit Anna University MEE all semester results direct link.