Applied machine learning and neural networks

Topic outline

• Select topic General

  General

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  • Select activity Course assessment plan

    

    Course assessment plan File
  • Select activity Kaggle.com

    

    Kaggle.com URL
  • Select activity Book

    

    Book URL

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• Select topic 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

  • Select activity Slides

    

    Slides File
  • Select activity Lab 1: Introduction to Data Analysis in Python

    

    Lab 1: Introduction to Data Analysis in Python Assignment
• Select topic 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
  

  • Select activity Slides

    

    Slides File
  • Select activity Lab2. General concepts

    

    Lab2. General concepts Assignment
• Select topic 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

  • Select activity Slides

    

    Slides File
  • Select activity Lab3. Distance-based algorighms

    

    Lab3. Distance-based algorighms Assignment
• Select topic Topic 4. Bayesian approach

  Topic 4. Bayesian approach

  Bayesian approach

  Probability density estimation

       - nonparametric
       - parametric

  • Select activity Slides

    

    Slides File
  • Select activity Lab4. Probability Density Estimation

    

    Lab4. Probability Density Estimation Assignment
• Select topic 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

  • Select activity Slides

    

    Slides File
  • Select activity Lab5. Logistic regression. ROC. AUC. One-hot encoding

    

    Lab5. Logistic regression. ROC. AUC. One-hot encoding Assignment
• Select topic 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

  • Select activity Slides

    

    Slides File
  • Select activity Lab 6. Neural Networks Basics

    

    Lab 6. Neural Networks Basics Assignment
  • Select activity Lab 7. Neural Nets Advanced

    

    Lab 7. Neural Nets Advanced Assignment
  • Select activity Lab 8. Pretrained Neural Nets

    

    Lab 8. Pretrained Neural Nets Assignment
• Select topic Topic 7. Rule-based learning

  Topic 7. Rule-based learning

  The concept of rule

  The criterion for the rule quality

  Finding rules

  Classification algorithms based on rules

  • Select activity Slides

    

    Slides File
• Select topic Topic 8

  Topic 8
• Select topic Topic 9

  Topic 9
• Select topic Topic 10

  Topic 10