Skip to content
CampusPrep
IT-802 (A) · Machine Learning/Official Syllabus

Machine Learning (IT-802 (A)) - Official Syllabus

1
Unit 1

Introduction

  • Introduction
  • Examples of various Learning Paradigms
  • Perspectives and Issues
  • Concept Learning
  • Version Spaces
  • Finite and Infinite Hypothesis Spaces
  • PAC Learning
  • VC Dimension
2
Unit 2

Supervised Learning Algorithms

  • Learning a Class from Examples
  • Linear
  • Non-linear
  • Multi-class and Multi-label classification
  • Decision Trees: ID3
  • Classification and Regression Trees (CART)
  • Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Neural Networks: Introduction
  • Perceptron
  • Multilayer Perceptron
  • Support vector machines: Linear and NonLinear
  • Kernel Functions
  • K-Nearest Neighbors
3
Unit 3

Ensemble Learning

  • Ensemble Learning Model Combination Schemes
  • Voting
  • Error-Correcting Output Codes
  • Bagging: Random Forest Trees
  • Boosting: Adaboost
  • Stacking
4
Unit 4

Unsupervised Learning

  • Introduction to clustering
  • Hierarchical: AGNES
  • DIANA
  • Partitional: K-means clustering
  • K-Mode Clustering
  • Self-Organizing Map
  • Expectation Maximization
  • Gaussian Mixture Models
  • Principal Component Analysis (PCA)
  • Locally Linear Embedding (LLE)
  • Factor Analysis
5
Unit 5

Probabilistic Learning

  • Bayesian Learning
  • Bayes Optimal Classifier
  • Naafve Bayes Classifier
  • Bayesian Belief Networks
  • Mining Frequent Patterns

Reference Books

  • Introduction to Machine Learning
  • Foundations of Machine Learning
  • Machine Learning
  • Data Classification Algorithms and Applications
  • Machine Learning An Algorithmic Perspective
  • Machine Learning: A Probabilistic Perspective
  • Data Mining Concepts and Techniques
  • Mathematics for Machine Learning
Go to where you left off?

Quick Add to Notes

Save questions, your own notes and screenshots into notes filed by unit. It takes a free account.

Create free account

Have an account? Log in