Unit 1 Supervised Learning (Regression/Classification)

9 lecture hours

• Basic methods: Distance-based methods
• Decision Trees
• Naive Bayes Linear models:Linear Regression
• Logistic Regression
• Generalized Linear Models Support Vector Machines
• Non linearity and KernelMethods Beyond Binary Classification: Multi-class/Structured Outputs

Unit 2 UnsupervisedLearning Clustering

7 lecture hours

• K-means/Kernel K-means Dimensionality Reduction PCA and kernel PCA Matrix Factorization and Matrix Completion GenerativeModels(mixture models and latent factor models)

Unit 3 Evaluating Machine Learning algorithms and Model Selection,

8 lecture hours

• Introduction to Statistical Learning Theory
• Ensemble Methods (Boosting
• Random Forests)

Unit 4 Sparse Modelling and Estimation, Modelling Sequence/Time-Series

• Deep Learning and Feature Representation Learning 8 20

Unit 5 Scalable Machine Learning (Online and Distributed Learning) A

8 lecture hours

• selection from some other advanced topics
• Semi-supervised Learning
• Active Learning
• Reinforcement Learning
• Inference in Graphical Models
• Introduction to Bayesian Learning and Inference

Unit 6 Recent trends in various learning techniques of machine learning and

5 lecture hours

• Classification methods for IOT applications
• Various models for IOT applications