[Coursera] Bayesian Methods for Machine Learning

About this course: Bayesian methods are used in lots of fields: 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 really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks 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 fully 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 be found with Bayesian methods.

Who is this class for: This course was designed for students with strong mathematical and machine learning background who want to get a different perspective of ML algorithms. Note that this is a very advanced course! You must have strong background in statistics, calculus and linear algebra.

Created by:  National Research University Higher School of Economics
National Research University Higher School of Economics
  • Taught by:  Daniil Polykovskiy, Researcher

    HSE Faculty of Computer Science
  • Taught by:  Alexander Novikov, Researcher

    HSE Faculty of Computer Science
Basic Info
Course 3 of 7 in the Advanced Machine Learning Specialization
Level Advanced
Commitment 6 weeks of study, 6 hours/week
Hardware Req Access to GPUs will be a plus, but you can complete the course without them.
How To Pass Pass all graded assignments to complete the course.
User Ratings
4.6 stars
Average User Rating 4.6See what learners said

Size: 2.20G


Add a Comment

Your email address will not be published. Required fields are marked *