Topic outline

  • General

  • Topic 1. Introduction to Machine learning. History

    Cybernetics and the first neural networks
    The first digital computers
    The emergence of expert systems
    Probabilistic approach
    Random Forest
    Recent achievements

  • Topic 2. General concepts

    Machine learning problem
    Classification and regression
    Features and their types
    Model and training method. Loss functional
    Probabilistic statement of the training problem. The maximum likelihood principle and its relation to empirical risk minimization.  Decision function
    Overfitting and the generalizing ability of an algorithm

  • Topic 3. Distance-based algorithms

    Generalized algorithm
    Examples: Nearest Neighbor, Parsen Window method
    The Curse of Dimensionality
    Choosing a distance
    Distance-based regression

  • Topic 4. Bayesian approach

    Bayesian approach

    Probability density estimation

         - nonparametric
         - parametric

  • Topic 5. Linear classification algorithms

    Linear regression
    Logistic regression
    Margins for a linear classifier
    One hot encoding
    ROC and AUC

  • Topic 6. Neural Networks

    Linear perceptron
    Stochastic gradient descent method
    Neural nets structure
    Objective functions for regression, binary/multiclass/multilabel classification
    Activation functions
    Neural nets design

  • Topic 7. Rule-based learning

    The concept of rule

    The criterion for the rule quality

    Finding rules

    Classification algorithms based on rules

  • Topic 8

  • Topic 9

  • Topic 10