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ML2021_lectures

This is a repository containing the lectures Machine Learning course (MA060018), which is held at Term 3, 2021.

The list of the current lectures published (will be updated with time):

  • LECTURE 1 (02.02) - Intro
  • LECTURE 2 (04.02) - Intro to regression [updated on 08.02]
  • LECTURE 3 (05.02) - Intro to classification [updated on 08.02]
  • LECTURE 4 (09.02) - SVM
  • LECTURE 5 (11.02) - Decision Trees
  • LECTURE 6 (12.02) - AdaBoost
  • LECTURE 7 (16.02) - Gradient Boosting
  • LECTURE 8 (18.02) - Imbalanced and Multi-Class Classification, Naive Bayes
  • LECTURE 9 (19.02) - Neural Networks
  • LECTURE 10 (20.02) - Model Selection and Feature Selection
  • LECTURE 11 (25.02) - Deep Learning
  • LECTURE 12 (02.03) - Bayesian Machine Learning
  • LECTURE 13 (04.03) - Gaussian Processes
  • LECTURE 14 (05.03) - Dimensionality Reduction
  • LECTURE 15 (09.03) - Anomaly Detection
  • LECTURE 16 (11.03) - Clustering

Course Description:

The course is a general introduction to machine learning (ML) and its applications. It covers fundamental modern topics in ML, and describes the most important theoretical basis and tools necessary to investigate properties of algorithms and justify their usage. It also provides important aspects of the algorithms’ applications, illustrated using real-world problems. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in depth fundamental ML algorithms for classification, regression, clustering, etc., their properties as well as their practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the methods above to crack various real-world problems. Home assignments include application of existing algorithms to solve applied industrial problems, development of modifications of ML algorithms. The students are assumed to be familiar with basic concepts in linear algebra, probability, calculus, optimization and python programming.

Full course syllabus:

The course syllabus and the other helpful information can be found in Canvas through the [link]

Seminars:

The seminars of the course can accessed via the link.

The list of the Teaching Assistants:

  • Alexander Korotin
  • Nikita Balabin
  • Egor Shvetsov
  • Evgenia Romanenkova
  • Nina Mazyavkina
  • Ruslan Rakhimov

You can contact the TAs via Canvas.

Contact regarding this github repo:

If you have any questions/suggestions regarding this githup repository or have found any bugs, please write to me at Nina.Mazyavkina

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