Machine Learning – MCA (054)

Table of Content

Machine Learning Techniques
(KCA- 054)
Unit I – Introduction – Learning, Types of Learning, Well defined learning problems, Designing a Learning System, History of ML, Introduction of MachineLearning Approaches – (Artificial Neural Network, Clustering, Reinforcement Learning, Decision Tree Learning, Bayesian networks, Support Vector Machine, Genetic Algorithm), Issues in Machine Learning and Data Science Vs Machine Learning;
Unit II – Regression: Linear Regression and Logistic Regression. Bayesian Learning – Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm.
Support Vector Machine: Introduction, Types of support vector kernel– (Linear kernel, polynomial kernel,and Gaussiankernel), Hyperplane – (Decision surface), Properties of SVM, and Issues in SVM.
Unit III – Decision Tree Learning- Decision tree learning algorithm, Inductivebias, Inductive inference with decision trees, Entropy and information theory, Information gain, ID-3 Algorithm, Issues in Decision tree learning.
Instance – Based Learning – k-Nearest Neighbour Learning, LocallyWeighted Regression, Radial basis function networks, Case-based learning.
Unit IV – Artificial Neural Networks – Perceptron’s, Multilayer perceptron,Gradient descent and the Delta rule, Multilayer networks, Derivation of Backpropagation Algorithm, Generalization, Unsupervised Learning – SOM Algorithm and its variant.
Deep Learning – Introduction,concept of convolutional neural network , Types of layers – (Convolutional Layers , Activation function , pooling , fully connected) , Concept of Convolution (1D and 2D) layers, Training of network, Case study of CNN for eg on Diabetic Retinopathy, Building a smart speaker, Self-deriving car etc.
Unit V – Reinforcement Learning – Introduction to Reinforcement Learning ,Learning Task,Example of Reinforcement Learning in Practice, Learning Models for Reinforcement – (Markov Decision process , Q Learning – Q Learning function, Q Learning Algorithm ), Application of Reinforcement Learning,Introduction to Deep Q Learning.
Genetic Algorithms: Introduction, Components, GA cycle of reproduction, Crossover, Mutation, Genetic Programming, Models of Evolution and Learning, Applications.