📘 AM502PC

Machine Learning

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ML Unit 1: Learning

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ML Unit 2: Multi-layer Perceptron

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ML Unit 4: Dimensionality Reduction and Evolutionary Learning

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ML Unit 5: Reinforcement Learning and Markov Chain Monte Carlo Methods

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ML Unit 1: Learning (alt)

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ML Unit 1: Assignment

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ML Mid 1

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Syllabus Overview

UNIT - 1: Learning

Learning

  • Types of Machine Learning
  • Supervised Learning
  • The Brain and the Neuron
  • Design a Learning System
  • Perspectives and Issues in Machine Learning
  • Concept Learning Task
  • Concept Learning as Search
  • Finding a Maximally Specific Hypothesis
  • Version Spaces and the Candidate Elimination Algorithm
  • Linear Discriminants: Perceptron, Linear Separability, Linear Regression

UNIT - 2: Multi-layer Perceptron

Multi-layer Perceptron

  • Going Forwards
  • Going Backwards: Back Propagation Error
  • Multi-layer Perceptron in Practice
  • Examples of using the MLP
  • Overview
  • Deriving Back-Propagation
  • Radial Basis Functions and Splines: Concepts, RBF Network, Curse of Dimensionality, Interpolations and Basis Functions
  • Support Vector Machines

UNIT - 3: Learning with Trees, Ensemble Learning and Basic Statistics

Learning with Trees

  • Decision Trees
  • Constructing Decision Trees
  • Classification and Regression Trees

Ensemble Learning

  • Boosting
  • Bagging
  • Different ways to Combine Classifiers

Basic Statistics

  • Gaussian Mixture Models
  • Nearest Neighbor Methods
  • Unsupervised Learning
  • K-means Algorithms

UNIT - 4: Dimensionality Reduction and Evolutionary Learning

Dimensionality Reduction

  • Linear Discriminant Analysis
  • Principal Component Analysis
  • Factor Analysis
  • Independent Component Analysis
  • Locally Linear Embedding
  • Isomap
  • Least Squares Optimization

Evolutionary Learning

  • Genetic Algorithms
  • Genetic Offspring: Genetic Operators, Using Genetic Algorithms

UNIT - 5: Reinforcement Learning and Markov Chain Monte Carlo Methods

Reinforcement Learning

  • Overview
  • Getting Lost Example

Markov Chain Monte Carlo Methods

  • Sampling
  • Proposal Distribution
  • Markov Chain Monte Carlo
  • Graphical Models: Bayesian Networks, Markov Random Fields, Hidden Markov Models, Tracking Methods
Machine Learning Notes