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Bayesian Methods for Machine Learning

Daniil Polykovskiy and Alexander Novikov
People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to...
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People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In this online HSE course we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases can be found with Bayesian methods. Do you have technical problems? Write to us: [email protected]
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by instructors who are recognized for their work in this topic
Covers Bayes methods in depth, which is both theoretically and practically important
Relevant to a wide range of fields, from game development to drug discovery
Provides hands-on experience through labs and interactive materials
Requires extensive background knowledge in probability and statistics
Does not provide a comprehensive overview of Bayesian methods

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Reviews summary

Bayesian methods for deep learning

This course offers a comprehensive introduction to Bayesian methods for machine learning, including their application to deep learning. It delves into the fundamentals of Bayesian statistics and provides practical experience through programming assignments. The course emphasizes mathematical rigor, but may require additional effort for those lacking a strong background in probability and statistics.
Course materials are relevant to real-world applications
"A good course for those who already have a good knowledge on Bayesian methods and want to deepen their knowledge."
Mathematical content presented in a clear and engaging manner
Assignments provide a challenging and rewarding learning experience
"I really like this course! The selection of course material and assignments was interesting, motivating, and challenging."
Demonstrates practical applications of Bayesian methods in machine learning
"Amazing course with the right balance of mathematics and practicals"
Provides strong theoretical grounding in Bayesian methods
"Solid math and statistic study materials and useful programming assignment."

Career center

Learners who complete Bayesian Methods for Machine Learning will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts gather, clean, and analyze data to identify trends and patterns. It is an excellent resource for Data Analysts as it covers the basics of Bayesian methods as well as intermediate level scenarios like automating the Bayesian workflow. It also provides an overview of Bayesian methods as applied to drug discovery and advanced concepts in deep learning.
Quantitative Analyst
Quantitative Analysts develop and apply mathematical and statistical models to assess financial risks and opportunities. This course is a comprehensive walkthrough of Bayesian fundamentals and more advanced topics in the field like Bayesian methods applied to deep learning and working with real-world case study examples.
Statistician
Statisticians collect, analyze, interpret, and present data. This course is ideal for Statisticians as it covers basic and advanced Bayesian methods. It can help them expand their knowledge or enhance their fundamentals.
Data Scientist
Data Scientists build models, design experiments, and interpret results to extract meaningful insights from data. This course is an excellent resource for new Data Scientists. It covers the fundamentals of Bayesian methods as well as techniques for working with deep learning models.
Actuary
Actuaries assess risk and uncertainty. They are often involved in pricing insurance policies and managing financial risk. This course covers the basics of Bayesian methods and their applications in fields like insurance and finance.
Machine Learning Scientist
Machine Learning Scientists work on algorithms, data, and models to assist computers in making predictions or taking actions. The use of Bayesian methods is not exclusive to Machine Learning but rather a technique that helps build a foundation for those new to the field. This course will walk through basic and more advanced techniques of applying the Bayesian method. More, experienced Machine Learning Scientists may find use in the section covering deep learning.
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. They typically require an advanced degree, and this course is a thorough introduction to Bayesian methods and their applications.
Risk Manager
Risk Managers assess and manage risks to an organization. They play a critical role in helping organizations make informed decisions about how to manage risk. This course is an excellent resource for Risk Managers to learn more about Bayesian methods and their applications in risk management.
Operations Research Analyst
Operations Research Analysts apply analytical methods to improve efficiency and decision-making in organizations. The section of this course that covers the application of Bayesian methods to deep learning may be particularly useful.
Health Economist
Health Economists apply economic principles to healthcare decision-making. This course is an excellent resource for Health Economists to learn more about Bayesian methods and their applications in healthcare.
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. The section of this course that covers Bayesian methods applied to drug discovery may be particularly useful.
Information Security Analyst
Information Security Analysts plan and implement security measures to protect an organization's computer networks and systems. It is an excellent resource for Information Security Analysts to learn more about Bayesian methods and their applications in advanced modern technologies such as deep learning.
Research Scientist
Research Scientists conduct scientific research in a variety of fields. This course is an excellent resource for Research Scientists to learn more about Bayesian methods and their applications in their specific fields.
Epidemiologist
Epidemiologists investigate the causes and spread of diseases. This course may be helpful to Epidemiologists as it covers the fundamentals of Bayesian methods and their applications to public health.
Software Engineer
Software Engineers design, develop, and maintain computer software programs. This course may be helpful in providing context to data science models and methodologies as applied to working software programs.

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We've selected 0 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Bayesian Methods for Machine Learning.

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