CS3491 Artificial Intelligence and Machine Learning Study Materials
Anna University – CS3491 Artificial Intelligence and Machine Learning Regulation 2021 Syllabus , Notes , Important Questions, Question Paper with Answers Previous Year Question Paper.
UNIT I PROBLEM SOLVING CS3491 Artificial Intelligence and Machine Learning Syllabus
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 CS3491 Artificial Intelligence and Machine Learning Notes
Acting under uncertainty – Bayesian inference – naïve bayes models. Probabilistic reasoning –
Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.
UNIT III SUPERVISED LEARNING CS3491 Artificial Intelligence and Machine Learning Important Questions
Introduction to machine learning – Linear Regression Models: Least squares, single & multiple variables,
Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function –
Probabilistic discriminative model – Logistic regression, Probabilistic generative model – Naive Bayes,
Maximum margin classifier – Support vector machine, Decision Tree, Random forests
UNIT IV ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING CS3491 Artificial Intelligence and Machine Learning Question Bank
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 CS3491 Artificial Intelligence and Machine Learning Question Paper
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.
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| Notes | Click Here |
| Important Questions | Click Here |
| Previous Year Question Paper | Click Here |
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TEXT BOOKS: CS3491 Artificial Intelligence and Machine Learning Notes
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.
REFERENCES: CS3491 Artificial Intelligence and Machine Learning Important Questions
1. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, 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
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