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# Introduction to Machine Learning (edX)

Introduction to Machine Learning (edX)

## Key Facts

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Provider
School
Language
Level
edX
ITMO University
English
Introductory

By enrolling in this online course you will spend approx. 5 Weeks/2–4 hours per week to learn key concept of Machine Learning.

## Course Overview

Learn the essentials of machine learning and algorithms of statistical data analysis.

Want to learn how to analyze the huge amounts of data? In this course you will learn modern methods of machine learning to help you choose the right methods to analyze your data and interpret the results correctly.

This course is an introduction to machine learning. It will cover the modern methods of statistics and machine learning as well as mathematical prerequisites for them. We will discuss the methods used in classification and clustering problems. You will learn different regression methods.

Various examples and different software applications are considered in the course. You will get not only the theoretical prerequisites, but also practical hints on how to work with your data in MS Azure.

## Course Syllabus

Week 1: Introduction to machine learning and mathematical prerequisites. The concepts of machine and statistical learning are introduced. We discuss the main branches of ML such as supervised, unsupervised and reinforcement learning, give specific examples of problems to be solved by the described approaches. Besides, we show that ML is not as powerful as one can think. Finally, we remind you of some basic concepts of mathematics used in further lectures.

Week 2: Regression (linear, polynomial, multivariable regression). Regression problem is one of the main problems in supervised learning. We start with the heuristic approach trying to solve a very practical problem and come to rigorous mathematical construction of the simple linear regression model. We go further and describe statistical properties of the model: confidence intervals for the model’s parameters, hypothesis testing of linear dependence. Finally, we come to a so-called multivariable linear and polynomial regressions and show some examples and applications.

Week 3: Logistic regression. The second branch of supervised learning is a classification problem. We deal with a two-class logistic regression and emphasise that it is not a regression at all. Then why is it called so? It’s construction is closely connected with linear regression described in the 2nd lecture. We remind you a maximum likelihood estimation method and its applications to logistic regression. Finally, we discuss some applications of the logistic regression to a football game predictions and describe ROC analysis or a quality testing approach for the described model.

Week 4: Naïve Bayes and K-nearest neighbors. In this lecture we continue with classification problem. We introduce a so-called naive Bayes approach to classification widely used in e-mail spam recognition until 2010. Then we come to a multi-class classification using K-nearest neighbors method. What are the metrics that we will use? How does a particular metric influence the result? What is K and how do you choose it solving a particular problem? These are the questions that are rigorously discussed in the lecture.

Week 5: Clustering methods: hierarchical and k-means clustering. Clusterization problem is at the heart of unsupervised learning. We have a lot of data and nothing else: we don’t know the amount of classes, similarities in objects, we know almost nothing. We show how to establish some order in the given chaotic data using hierarchical clustering method and k-means approach. How to establish the initial clusters, what metric to choose, what actually means “close and far” objects? These questions are discussed in the lecture.

### Olga Egorova

Olga is an assistant professor at the Higher School of Digital Culture at ITMO University. She received her Ph. D in philology from St. Petersburg State University in 2005.

### Natalia Grafeeva

Natalia is an associate professor at the Higher School of Digital Culture at ITMO University. She received her Ph.D. in mathematics and the academic rank of associate professor from St. Petersburg State University in 1980 and 1995, respectively.

### Elena Mikhailova

Elena is the Director of Higher School of Digital Culture at ITMO University, an Advisor to Rector’s Office for Digital Culture. She received her Ph. D. in mathematics from St. Petersburg State University in 1997.

### Dmitry Volchek

Dmitry Volchek is an associate professor at the Higher School of Digital Culture at ITMO University. He received his master’s engineer degree in Information technologies in Education and Ph.D.in computer science from ITMO University in 2012 and 2019, respectively.

### Anton Boitsev

Anton is an associate professor at the Higher School of Digital Culture at ITMO University. He received his bachelor’s and master’s degree in mathematics and Ph.D. in mathematical and theoretical physics from ITMO University in 2013, 2015 and 2019, respectively.

### Aleksei Romanov

Aleksei Romanov is an associate professor at the Higher School of Digital Culture at ITMO University. He received his master’s engineer degree in Information technologies in Education and Ph.D.in computer science from ITMO University in 2012 and 2019, respectively

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