mhmohassan/bootcamp-idao-2022

IDAO 2022: Machine Learning Bootcamp

IDAO 2022 ML Bootcamp

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As a warm-up before IDAO 2022, we decided to organize the Machine Learning Bootcamp, where we will train participants for future ML competitions. The boot camp is a two-day intensive training that will help you increase your ML knowledge, acquire practical skills in ML contests and learn basic deep learning tools. It will be useful for those making the first steps in the world of ML contests.

The event is open to all undergraduate and postgraduate students. Participation is free, you just need to register. There are no strict prerequisites, but potential participants should at least be familiar with classic ML algorithms (like linear models and decision trees); pandas, NumPy, Matplotlib, and sklearn libraries.

Event timetable

December 4

• 12:00-12:05 Intro: boot camp goals, schedule, and topics. Presentation of speakers. (by Sergey Karapetyan)
• 12:05-13:20 Categorical Data Encoding Techniques. Practical tricks in data analysis tasks. (by Anastasia Maximovskaya)
• 13:30-14:30 Boosting Algorithms in Machine Learning. (by Elena Kantonistova)
• 15:00-16:15 Some interesting non-classical applications of machine learning. Machine learning on graph-structured data. (by Ildus Sadrtdinov)
• 16:45-17:00 Highlights on Data Analysis Competitions. Description of the task in the training contest. (by Alexey Birshert)
• 17:00 Start of the 24-hour training contest.

December 5

• 17:00 The end of the training contest
• 17:00-18:15 Introduction to Deep Learning: basic principles of neural networks, areas of application. Pytorch framework. (by Artur Petrosyan)
• 18:30-20:00 Presentations of the best solutions for the training contest. Description of the solution prepared by the boot camp team. (by Alexey Birshert)

Notice! Time is indicated in the Moscow timezone (GMT+3).

The boot camp is prepared together with HSE University’s Machine Learning and Data-Intensive Systems master’s programme. This online master's programme enables students to gain practical experience in solving machine learning problems and enhance their skills in developing data-intensive systems. The programme is aimed at students who want to delve into machine learning, gain a deep understanding of ML algorithm theory, and acquire practical experience solving data analysis problems.