Bayesian Methods for Machine Learning
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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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| Rating | 4.3★ based on 122 ratings |
|---|---|
| Length | 7 weeks |
| Effort | 6 weeks of study, 6 hours/week |
| Starts | Jan 24 (118 weeks ago) |
| Cost | $49 |
| From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
| Instructors | Daniil Polykovskiy, Alexander Novikov |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Machine Learning |
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What people are saying
course on bayesian
It is proper university level course on Bayesian methods.
The first and best indepth course on Bayesian methods.
The only solid online course on Bayesian ML methods!
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final project
However, the introduction of project assignments are very confusing, especially the final project.
I really like the final project.
Really the technical problems in the final project are too extreme.
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programming assignments
Really regret for lacking the time to finish all the programming assignments.
This class provided excellent lectures and very instructive programming assignments.
The programming assignments contain a lot of missing or inconsistent instructions.
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not enough
So far the most interesting course in specialisation Good course.Too much theory, not enough practice Various advanced Machine Learning topics like Bayesian interpretation techniques, probabilistic modelling, variational auto encoders, etc.
Slides nor audio transcripts, which are less rigorous, are not enough to cover such difficult and technical topics ***Also the peer review is cumbersome and for me doesn't add value and slows down the certification process.
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probability theory
Many more theoretical formulas and derivations than previous courses of the specialization, which might require quite a bit of probability theory knowledge.
A wonderful course to improve the theoretical understanding of machine learning and recap probability theory.
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em algorithm
The lecturers did their best to drag the listener through the math of the EM algorithm and more.
I really learned a lot about Bayesian methods, especially EM algorithm, Variational Inference, VAE, but still did not understand LDA, Bayesian optimization well.
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at times
There are "tricks" in the quizes and the answers are not-obvious at times, or there are caveats unknown to you.
Unfortunately, the notation is a little sloppy and inconsistent at times throughout the lectures.
The assignment environment setup was a bit cumbersome at times, but the level of difficulty in the assignments really solidified the understanding of the course material.
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well prepared
The course material is very well prepared and self-contained.
However I find material not well prepared (defficient mathematical notation).
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| Rating | 4.3★ based on 122 ratings |
|---|---|
| Length | 7 weeks |
| Effort | 6 weeks of study, 6 hours/week |
| Starts | Jan 24 (118 weeks ago) |
| Cost | $49 |
| From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
| Instructors | Daniil Polykovskiy, Alexander Novikov |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Machine Learning |
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