Machine Learning

Here I write about some techniques to make machines learn from data.

Topics:

• Concept Learning

  • Find-S Algorithm

  • Version Space

  • List-Then-Eliminate Algorithm

  • Representing Version Spaces

  • Candiate-Elimination algorithm

  • Inductive Bias

• Decision Trees

  • When to consider decision trees

  • Top-down induction of decision trees

  • Entropy and Information Gain

  • ID3

  • Extensions of ID3

• Basics of Data Mining

  • Outliers

  • Missing Value

  • Distance Function

• Clustering

  • Distance Measures

  • Bias in clustering

  • Hierachical clustering

  • Optimization based clustering

  • Compression by clustering

  • K-means clustering

  • Soft clustering

  • Conceptual clustering: Cobweb

• Dimensionality Reduction

  • Curse of dimensionality

  • Principal component analysis

  • Principal curves

  • Visualization of high dimensional data

• Neural Networks

  • Computer vs. brain

  • Types of learning

  • Hebbian learning

  • Perceptron

  • Multilayer Perceptron

  • Neural architectures

• Local Methods

  • Instance-based learning

  • Radial basis functions

  • Self-organizing maps

• Classification

  • Bayes classifier

  • Euclidean classifier

  • Support vector machine

  • Random forests

• Reinforcement Learning
• Modelling Unvertainty