Math: Bayesian Inference for Machine Learning Part II

Machine Learning Tokyo
Machine Learning Tokyo
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Welcome to our second ML Math Lecture. 🙂

We are looking at Bayesian Inference for Machine Learning with gaussian distribution. Hiroshi Urata will show how to deal with some mathematical expressions such as derivation of posterior distribution and predictive distribution. The lecture is designed for entry level and we welcome everyone who is interested in the mathematics behind machine learning.

-- AGENDA --
10:30 - 10:40 : Welcome note
10:40 - 10:45 : Quick review of first ML MATH session
10:45 - 11:20 : Introduction of probability distribution, property of gaussian distribution
11:20 - 11:35 : Break
11:35 - 12:20 : Bayesian Inference with gaussian distribution
12:20 - 12:30 : Closing

Hiroshi Urata is a Data Scientist at IBM Japan. He runs a blog on Machine Learning, covering topics such as Machine Learning Math, Deep Learning, Natural Language Processing and more.

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