Page: Syllabus of Book of Machine Learning*

As per Choice Based Grading System

Module 01: Introduction to Machine Learning

  • Machine Learning,
  • Types of Machine Learning,
  • Issues in Machine Learning,
  • Application of Machine Learning,
  • Steps in developing a Machine Learning Application.

Module 02: Introduction to Neural Network

  • Introduction -Fundamental concept- Evolution of Neural Networks- Biological Neuron,
  • Artificial Neural Networks,
  • NN architecture,
  • Activation functions,
  • Mc Culloch-Pitts Model.

Module 03: Introduction to Optimization Techniques:

  • Derivative based Optimization- Steepest Descent,
  • Newton method.
  • Derivative free Optimization- Random Search,
  • Down Hill Simplex.

Module 04: Learning with Regression and trees:

  • Learning with Regression: Linear Regression, Logistic Regression.
  • Learning with Tress: Decision Tress, Constructing Decision Tress using Gini Index, Classification and Regression Tress (CART)

Module 05: Learning with Classification and clustering:

  • Classification: Rule based classification, classification by Bayesian Belief networks, Hidden Markov Models.
  • Support Vector Machine: MaximumMargin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.
  • Clustering: Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.

Module 06: Dimensionality Reduction:

  • Dimensionality Reduction Techniques,
  • Principal Component Analysis,
  • Independent Component Analysis,
  • Single value decomposition.
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written 29 days ago by gravatar for Sanket Shingote Sanket Shingote ♦♦ 250
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