Master
2023/2024
Machine Learning I
Type:
Compulsory course (Science of Learning and Assessment)
Area of studies:
Psychology
Delivered by:
Department of Educational Programmes
Where:
Institute of Education
When:
1 year, 4 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Science of Learning and Assessment
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.
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
- Introduction
- Classification
- Neural networks: predicting
- Neural networks: learning
- Applied machine learning and decision trees
- Reinforcement learning
Interim Assessment
- 2023/2024 4th module0.3 * Attendance and participation + 0.4 * Final exam + 0.3 * Midterm
