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Regular version of the site
2022/2023

Machine Learning I

Type: Mago-Lego
When: 4 module
Open to: students of all HSE University campuses
Language: English
ECTS credits: 3
Contact hours: 30

Course Syllabus

Abstract

Prerequisite: Basic experience in Python. Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is valuable part of modern scientific experiments, in which results could be explained and analyzed, and substantiated with eligible proof. The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering, dimensionality reduction, scalable algorithms. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The goal of this course is to teach students to work with, analyze and apply it to data obtained during experiments that evaluate biological, behavioural, neuroimaging parameters, or a combination of these.
Learning Objectives

Learning Objectives

  • Learning the basic concepts and methods used in Machine Learning.
Expected Learning Outcomes

Expected Learning Outcomes

  • Get a glimpse of the state of affairs in machine learning and review logistic classification
  • Learn to implement gradient descent and regularization, and apply support vector machines
  • Learn the operation and training of neural networks, and their relation to deep learning
  • Grasp the basics and how to run decision tree learning models, and scalable implementations
  • Getting acquainted with the main unsupervised learning teachniques
  • Learn the basic concepts and uses of reinforcement learning algorithms
  • Read articles devoted to various applications of ML in education and analyze them
Course Contents

Course Contents

  • Introduction
  • Classification
  • Neural networks: predicting
  • Neural networks: learning
  • Applied machine learning and decision trees
  • Reinforcement learning
  • Machine Learning in educational research
    I will briefly talk about our research where we use convolutional neural networks to analyze images and GPT-3 to generate texts for school tasks.
Assessment Elements

Assessment Elements

  • non-blocking Midterm 1
  • non-blocking Midterm 2
    Programming assignment
  • non-blocking Final exam
  • non-blocking Midterm3
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.2 * Midterm3 + 0.2 * Midterm 2 + 0.2 * Midterm 1 + 0.4 * Final exam
Bibliography

Bibliography

Recommended Core Bibliography

  • The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017

Recommended Additional Bibliography

  • Pattern recognition and machine learning, Bishop, C. M., 2006

Authors

  • MARTINEZ-SAITO MARIO -