1 of 8

DSE 4 - Machine Learning

2 of 8

Course Objectives

The course aims at introducing the basic concepts and techniques of machine learning so that a student can apply machine learning techniques to a problem at hand.

3 of 8

Course Learning Outcomes

On successful completion of this course, the student will be able to:

  1. Differentiate between supervised and unsupervised learning tasks.
  2. Differentiate between linear and non-linear classifiers.
  3. Describe theoretical basis of SVM.
  4. Implement various machine learning algorithms learnt in the course.

4 of 8

Unit-1 Introduction

  • Basic Definitions,
  • Hypothesis Space and Inductive Bias,
  • Bayes Optimal Classifier and Bayes error,
  • Occam's Razor,
  • Curse of Dimensionality,
  • Dimensionality Reduction,
  • Feature scaling, feature selection methods.

5 of 8

Unit-2 Regression

  • Linear regression with one variable,
  • Linear regression with multiple variables,
  • Gradient Descent,
  • Logistic Regression,
  • Over-fitting, Regularization.
  • Performance Evaluation Metrics,
  • Validation Methods

6 of 8

Unit-3 Classification

  • Decision trees,
  • Naive Bayes classifier, k-nearest neighbor classifier,
  • Perceptron, multilayer perceptron,
  • Neural networks, back-propagation algorithm,
  • Support Vector Machine (SVM), Kernel functions.

7 of 8

Unit-4 Clustering

  • Approaches for clustering,
  • Distance metrics, K-means clustering,
  • Expectation maximization,
  • Hierarchical clustering,
  • Performance evaluation metrics, validation methods.

8 of 8

Practicals

For practical Labs for Machine Learning, students may use softwares like MATLAB/Octave or Python. For later exercises, students can create/use their own datasets or utilize datasets from online repositories like UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/).

Refer to the list of practicals for more details.