Course

Unit 1

Basic motivation, examples of machine learning applications, Supervised and Unsupervised Learning – Review linear algebra, vector spaces, linear transformations, eigenvalues, and vectors – Review of statistics and probability theory, random variables, and probability distributions. Basic concepts of fuzzy sets – Operations on fuzzy sets –Fuzzy relation equations – Fuzzy logic control – Fuzzification – Defuzzification – Knowledge base – Decision making logic – Membership functions – Rule base

Unit 2

Multiple Variable Linear regression, Multiple regression, Logistic regression, K-NN classification, Naive Bayes classifiers, and Support vector machines.

K-means clustering, Hierarchical clustering, High-dimensional clustering, Dimension ReductionPCA, Ensemble techniques Decision Trees, Random Forests, Bagging, Boosting-Value based methods Q-learning.

Reinforced learning.

Unit 3

Introduction – history of neural networks – multilayer perceptrons –Back propagation algorithm and its variants – Different types of learning, examples, Deep learning – Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM). Generative Adversarial Networks (GANs). Model Evaluation and Validation – Cross-validation techniques, Evaluation metrics for regression and classification tasks, Bias-variance tradeoff, Feature Engineering, and Model Optimization.

Course Objective

• Provide a strong foundation of fundamental concepts in Artificial Intelligence
• Elobarate different AI and machine learning techniques for design of AI system

Course Outcomes

CO-PO Mapping

Skills Acquired

Formulate engineering problems as a machine learning problem; Select appropriate solution methods and strategies to solve machine learning problems; Solve engineering design-related machine learning problems using software tools.

1. Chandra S.S.V Artificial Intelligence and Machine Learning, Prentice Hall India Learning Private Limited; 4th edition, 2018.
2. Tom Mitchell, “Machine Learning”, McGraw Hill, 1997.
3. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press,
4. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
5. Muller and S. Guido, “Introduction to Machine Learning with Python”, O’Reilly Media, 2017.
6. Goodfellow, YoshuaBengio and Aeron Courville,” Deep Learning”, MIT Press, First Edition,
7. 6Guttag, , “Introduction to Computation and Programming Using Python: With Application to Understanding Data”, Second Edition. MIT Press, 2016.

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