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