Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

## Overview

Advanced methods of machine learning. You will learn how to analyze big amounts of data, to find regularities in your data, to cluster or classify your data.

In this course you will learn specific concepts and techniques of machine learning, such as factor analysis, multiclass logistic regression, resampling and decision trees, support vector machines and reinforced machine learning.

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

## Syllabus

Week 1 : Factor analysis . Quite often the amount of variables in the data set under analysis is large, thus the data can not be visualized. This implies a very theoretical approach to obtain some trends or dependencies in the data. Factor analysis is a commonly used machine learning technique to reduce the amount of variables in a dataset. We will thoroughly discuss principal component analysis, but will consider also other factor analysis methods.

Week 2: Multiclass logistic regression . Multiclass logistic regression (or multinomial regression) is a classification method generalizing logistic regression to multiclass case, i.e. when there are more than two possible outcomes. Multiclass LR is used when the dependent variable is nominal and for which there are more than two categories.

Week 3: Resampling and decision trees. Resampling methods are essential to test and evaluate statistical models. For instance, you could draw several samples and then assess the variability and stability of your model on different samples. Decision trees are intuitive concepts for making decisions. They are also widely used for regression and classification. You split all your observations into a number of samples, and predictions are made based on the mean or mode of the training observations in that sample.

Week 4: Support vector machines. SVM is a supervised learning models that are used for classification or regression analysis. We will thoroughly consider a more simple and intuitive classifier called the optimal margin classifier and then proceed to a generalized SVM.

Week 5: Reinforced machine learning. Main principles of reinforcement learning are discussed, that is how to maximize the cumulative feedback of an object’s actions in case when an object interacts with the environment and receives a positive or negative feedback from the environment to its actions. Q-learning method will be considered in details.

### Taught by

Anton Boitsev, Aleksei Romanov, Dmitry Volchek, Elena Mikhailova, Natalia Grafeeva and Olga Egorova

## Reviews

4.6 rating, based on 5 reviews

• Anonymous

Anonymous completed this course.

One of the best courses in ML, it covers the mathematical part deeply and in great detail, this is the right course if you want to rigorously base ML algorithms mathematically, the readings in each section are very well structured

You have to dedicate time to the course to understand the concepts in a concrete way, the instructors are always attentive to questions and discussions, without a doubt one of the best ML courses I have taken so far, I hope this course becomes well known and people who want to delve into the mathematical part of ML concepts take it, it's really worth it
• Anonymous

Anonymous completed this course.

The course was a good experience in general. The theory part of topics is well covered in the videos and the grading assignments take care of actual implementation of those concepts. Although you don't get to code a lot, if you can understand and experiment with the provided code later. The quizzes in-between the course videos help a lot in better understanding the working of several concepts explained in videos. Videos are a bit monotonous but the explanation is good if you persist!
Last but certainly not the least, the course instructors are very supportive, helpful and prompt. All queries and issues were addressed and resolved very quickly. Overall a good journey.
• Irfan Khorakiwala

Irfan Khorakiwala completed this course.

This course has a good mathematical rigor on PCA, SVM and Random Forests. The best thing about the course is that any questions that were raised in the discussion threads were promptly answered and the answers had mathematical proofs. I would like to suggest that a basic understanding of Linear Algebra should be a pre-requisite for this course. Overall a wonderful course.
• Anonymous

Anonymous completed this course.

This is the best course I have taken on EdX. The course material is great, the layout and presentation is great and the assignments were fruitful. I had been looking for a course that had the mathematical explanations of ML algorithms and this is the only one that explained in detail every algorithm. This is a must take course for every aspiring ML engineer
• Anonymous

Anonymous completed this course.

It was an interesting course based upon mathematics and with interesting exercices from real problems. The documentation is useful, both the handouts and the python notebooks.

### Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.