How to Win a Data Science Competition: Learn from Top Kagglers

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент больших данных и информационного поиска

Статус:

Курс по выбору

Когда читается:

4-й курс, 2, 3 модуль

Преподаватели

Биршерт Алексей Дмитриевич

Садртдинов Ильдус Рустемович

Шабалин Александр Михайлович

Course Syllabus

Abstract

Our course is based on the ML-Training program, which takes place within the framework of the joint project of the Higher School of Economics and MTS. We will familiarize ourselves with the three main domains (tabular data, natural language processing, computer vision). We will look at classical and advanced approaches, examples of tasks from the Kaggle collection.

Learning Objectives

• To study the modern approaches to fitting high-performance models for real-world data analysis problems
• To master modern tools for building machine learning models
• To learn how to preprocess the data and generate new features from various sources such as text and images
• To know the basics of exploratory data analysis
• To be able to quickly come up with simple models for solving problems and know the logic of complicating them to improve quality
• To be able to find features in data: omissions, inaccuracies, anomalous values, etc.

Expected Learning Outcomes

• Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance.
• Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data.
• Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them.
• Get exposed to past (winning) solutions and codes and learn how to read them.
• Master the art of combining different machine learning models and learn how to ensemble.

Course Contents

• Strategies for participation in competitions
• Tricks of the deep learning
• Leaks in the data and how to use them

Assessment Elements

• Online course
  Coursera course “How to Win a Data Science Competition: Learn from Top Kagglers”
• Competition

Interim Assessment

• 2021/2022 3rd module
  0.6 * Competition + 0.4 * Online course

Bibliography

Recommended Core Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
• Mehryar Mohri, Afshin Rostamizadeh, & Ameet Talwalkar. (2018). Foundations of Machine Learning, Second Edition. The MIT Press.

Authors

• BIRSHERT ALEKSEY DMITRIEVICH
• Sadrtdinov Ildus Rustemovich
• SHABALIN ALEKSANDR MIKHAILOVICH