Bachelor
2024/2025
How to Win a Data Science Competition: Learn from Top Kagglers
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
4 year, 3 module
Mode of studies:
distance learning
Online hours:
32
Open to:
students of all HSE University campuses
Language:
English
ECTS credits:
5
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.
Assessment Elements
- Homework 1tabular data
- Homework 2natural language processing
- Homework 3computer vision
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.
Recommended Additional Bibliography
- Cady, F. (2017). The Data Science Handbook. Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1456617