Module 01: Introduction to Machine Learning
- Machine Learning,
- Types of Machine Learning,
- Issues in Machine Learning,
- Application of Machine Learning,
- Steps in developing a Machine Learning Application.
Module 02: Introduction to Neural Network
- Introduction -Fundamental concept- Evolution of Neural Networks- Biological Neuron,
- Artificial Neural Networks,
- NN architecture,
- Activation functions,
- Mc Culloch-Pitts Model.
Module 03: Introduction to Optimization Techniques:
- Derivative based Optimization- Steepest Descent,
- Newton method.
- Derivative free Optimization- Random Search,
- Down Hill Simplex.
Module 04: Learning with Regression and trees:
- Learning with Regression: Linear Regression, Logistic Regression.
- Learning with Tress: Decision Tress, Constructing Decision Tress using Gini Index, Classification and Regression Tress (CART)
Module 05: Learning with Classification and clustering:
- Classification: Rule based classification, classification by Bayesian Belief networks, Hidden Markov Models.
- Support Vector Machine: MaximumMargin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.
- Clustering: Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.
Module 06: Dimensionality Reduction:
- Dimensionality Reduction Techniques,
- Principal Component Analysis,
- Independent Component Analysis,
- Single value decomposition.