ML2023_lectures

This is a repository containing the lectures for the Skoltech's Machine Learning course (MA060018), which is held at Term 3, 2023.

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

• LECTURE 1 (31.01) - Introduction Lecture
• LECTURE 2 (02.02) - Regression
• LECTURE 3 (03.02) - Classification
• LECTURE 4 (07.02) - Support Vector Machines
• LECTURE 5 (09.02) - Tree-based methods, Bagging, Random forest
• LECTURE 6 (10.02) - Adaboost
• LECTURE 7 (14.02) - Gradient Boosting
• LECTURE 8 (16.02) - Multi-class predictions, Naive Bayes
• LECTURE 9 (17.02) - Model and feature selection
• LECTURE 10 (21.02) - Artificial Neural Networks
• LECTURE 11 (02.03) - Deep Artificial Neural Networks
• LECTURE 12 (03.03) - Sequential Data
• LECTURE 13 (07.03) - Gaussian Processes
• LECTURE 14 (09.03) - Dimensionality Reduction
• LECTURE 15 (10.03) - Anomaly Detection
• LECTURE 16 (14.03) - Clusterization

Course Description:

The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. 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, and 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 ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.

On completion of the course students are expected to:

• Have a good understanding of the fundamental issues and challenges of ML: data, model selection, model complexity among others;
• Have an understanding of the strengths and weaknesses of many popular ML approaches;
• Appreciate the basic underlying mathematical relationships within and across ML algorithms and the paradigms of supervised and unsupervised learning.
• Be able to design and implement various machine learning algorithms in a range of real-world applications.

Seminars:

The seminars of the course can be accessed via the link.

Contact regarding this github repo:

If you have any questions/suggestions regarding this github repository or have found any bugs, please write to me at [email protected]