Practical Content :-
- Basic concepts of data processing using mean, median, standard deviation, variance, covariance, hypothesis testing.
- KNN algorithm for classification problems
- Linear Regression and multiple linear regression for modeling the relationship between a scalar dependent variable and one or more independent variables
- Naïve Bayesian classifier for supervised classification
- The various activation functions for ANN
- single-layer perceptron Learning and testing Algorithm for prediction
- Multilevel perceptron Learning and testing Algorithm for prediction
- Back-propagation Learning and testing Algorithm for prediction
- K-Means for clustering
- Principal component analysis for dimensionality reduction
- KSOM for dimensionality reduction and unsupervised classifier
- Monte Carlo method of board class of computational algorithms that rely on repeated random sampling to obtain numerical results
Machine Learning - Machine Learning Foundations –Overview – Design of a Learning system - Types of machine learning –Applications Mathematical foundations of machine learning - random variables and probabilities - Probability Theory – Probability distributions -Decision Theory- Bayes Decision Theory - Information Theory
Linear Models for Regression - Linear Models for Classification – Naïve Bayes - Discriminant Functions -Probabilistic Generative Models -Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees - Classification Trees- egression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Back- propagation. Support vector machines - Ensemble methods- Bagging- Boosting.
Introduction, Feature Selection and Feature Extraction, Principle Component Analysis, Linear Discriminant Analysis, Factor Analysis
Neural Network and Support Vector Machine:
Introduction to NN, Single Layer Perceptron, Multi-Layer Perceptron, Feed-forward Network Functions, Back- Propagation, Multi-Layer Perceptron with Gradient Descent. Support Vector Machines – Optimal Separation, Kernels
Introduction to Clustering, Advanced Clustering Methods, Cluster Evaluation
RNN, CNN, Overview of reinforcement learning, markov decision processes, Q-Learning & SARSA Learning, Applications of Reinforcement Learning.
Evolutionary Learning, Markov chain monte carlo methods, GMM.