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 the basic concepts and methods used in Machine Learning.
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
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.
Instructors
Gracheva, Daria
Martinez-Saito, Mario
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors