Topic outline
- General
- Topic 1. Introduction to Machine learning. History
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
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
Topic 3. Distance-based algorithms
Generalized algorithm
Examples: Nearest Neighbor, Parsen Window method
Margin
The Curse of Dimensionality
Choosing a distance
Distance-based regression - Topic 4. Bayesian approach
Topic 4. Bayesian approach
Bayesian approach
Probability density estimation
- nonparametric
- parametric - Topic 5. Linear classification algorithms
Topic 5. Linear classification algorithms
Linear regression
Logistic regression
Margins for a linear classifier
One hot encoding
ROC and AUC
SVM - Topic 6. Neural Networks
Topic 6. Neural Networks
Linear perceptron
Stochastic gradient descent method
Neural nets structure
Backpropagation
Objective functions for regression, binary/multiclass/multilabel classification
Activation functions
Neural nets design - Topic 7
Topic 7
- Topic 8
Topic 8
- Topic 9
Topic 9
- Topic 10
Topic 10