CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]
Topics with syllabus and resources
100.00 Introduction to Machine Learning
- What is Machine Learning?, Key Terminology, Types of Machine Learning, Issues in Machine Learning, Application of Machine Learning, How to choose the right algorithm, Steps in developing a Machine Learning Application.
200.00 Learning with Regression
- Linear Regression, Logistic Regression.
300.00 Learning with Trees
- Using Decision Trees, Constructing Decision Trees, Classification and Regression Trees (CART).
400.00 Support Vector Machines (SVM)
- Maximum Margin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.
500.00 Learning with Classification
- Rule based classification, classification by backpropoagation, Bayesian Belief networks, Hidden Markov Models.
600.00 Dimensionality Reduction
- Dimensionality Reduction Techniques, Principal Component Analysis, Independent Component Analysis.
700.00 Learning with Clustering
- K-means clustering, Hierarchical clustering, Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.
800.00 Reinforcement Learning
- Introduction, Elements of Reinforcement Learning, Model based learning, Temporal Difference Learning, Generalization, Partially Observable States.
Question Papers For All Subjects
- Software Architecture 2011 to 2018
- Multimedia System Design 2011 to 2015
- Distributed Computing 2010 to 2016
- Data Warehousing and Mining 2010 to 2018
- Human Machine Interaction 2016 to 2018
- Parallel and Distributed Systems 2016 to 2018