Machine Learning Theory

Get your team up to speed with our hands-on deep training on Machine Learning. Based on the materials presented at top academic conferences.

What is it about?

This deep dive training covers all aspects of machine learning and considers all major problems common in practical applications: classification, regression, clustering, and dimensionality reduction. We present numerous methods and techniques for solving these problems, both classical and new, created in the last five years. The emphasis is on the deep understanding of the mathematical fundamentals, relationships, strengths and weaknesses of the methods.

Each algorithm or technique is presented following the same standardized scheme: (1) the original ideas and heuristics; (2) formalization of the ideas; (3) the necessary background material and mathematical theory; (4) the conceptual formalization of the algorithm in pseudocode; (5) the algorithm's analysis, including its strengths, weaknesses, and the limits of applicability; (6) possible ways to address deficiencies in practical projects; (7) the comparison with other methods. We start from the basics, which require only minimal prior knowledge of linear algebra and statistics, and build up to the state-of-the-art ideas.

By the end of the training, the participants will be able to build their own end-2-end machine learning pipelines and make intelligent decisions at each step of the process following the well-recognized CRISP-DM methodology.

Training Content