Sr. No.

Content

Total Weightage 

Introduction to Machine Learning:

Introduction, Different Types of Learning, Hypothesis Space, Inductive Bias, Evaluation and
Cross Validation

Basic Machine Learning Algorithms:

Linear Regression, Decision Trees, Learning Decision Trees, K-nearest Neighbour,
Collaborative Filtering, Overfitting

Dimensionality Reduction:

Feature Selection, Feature Extraction

Bayesian Concept of Learning:

Bayesian Learning, Naïve Bayes, Bayesian Network, Exercise on Naïve Bayes

Logistic Regression and Support Vector Machine:

Logistic Regression, Introduction to Support Vector Machine, The Dual Formation,
Maximum Margin with Noise, Nonlinear SVM and Kernel Function, SVM: Solution to the
Dual Problem

Basics of Neural Network:

Introduction to neural network, Multilayer Neural Network, Neural Network and
Backpropagation Algorithm, Deep Neural Network

Computation and Ensemble Learning:

Introduction to Computation Learning, Sample Complexity: Finite Hypothesis Space, VC
Dimension, Introduction to Ensembles, Bagging and Boosting

Basic Concepts of Clustering:

Introduction to Clustering, K-means Clustering, Agglomerative Hierarchical Clustering