JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To learn the concept of how to learn patterns and concepts from data without being explicitly programmed in various IOT nodes.
• To design and analyse various machine learning algorithms and techniques with a modern outlook focusing on recent advances.
• Explore supervised and unsupervised learning paradigms of machine learning.
• To explore Deep learning technique and various feature extraction strategies.

Course Outcomes

After completion of course, students would be able to:

• Extract features that can be used for a particular machine learning approach in various IOT applications.
• To compare and contrast pros and cons of various machine learning techniques and to get an insight of when to apply a particular machine learning approach.
• To mathematically analyse various machine learning approaches and paradigms.

UNIT - I

Supervised Learning (Regression/Classification) Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes. Linear models: Linear Regression, Logistic Regression, Generalized Linear Models. Support Vector Machines, Nonlinearity and Kernel Methods. Beyond Binary Classification: Multi-class/Structured Outputs, Ranking.

UNIT - II

Unsupervised Learning: Clustering: K-means/Kernel K-means. Dimensionality Reduction: PCA and kernel PCA. Matrix Factorization and Matrix Completion. Generative Models (mixture models and latent factor models).

UNIT - III

Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)

UNIT - IV

Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and Feature Representation Learning

UNIT - V

Scalable Machine Learning (Online and Distributed Learning) A selection from some other advanced topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in Graphical Models, Introduction to Bayesian Learning and Inference. Recent trends in various learning techniques of machine learning and classification methods for IOT applications. Various models for IOT applications.

References:

• 1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 Page 11 of 55 R19 M.TECH. CSE/CS
• 2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Springer 2009 (freely available online)
• 3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 Page 12 of 55 R19 M.TECH. CSE/CS M. Tech - CSE/CS – I Year – I Semester Common to CSE and CS Syllabus