";s:4:"text";s:3665:" Bayesian methods allow for more accurate predictions under uncertainty and with less amount of data. If you have worked with Machine Learning and not given Bayesian inference much attention, I would say it is definitely something to look into. He has published several papers in the area of Bayesian Machine Learning including a book titled When applied to very complex models such as Deep Neural Networks, they would help in automatic tuning of hyperparameters and efficient compression of the models. Dr. Koduvely will present a brief introduction to Bayesian Inference and some examples of machine learning models where Bayesian methods are used very effectively.Prior to moving to Canada, Dr. Koduvely worked as Senior/Principal Data Scientist for Amazon and Samsung R&D Institute in Bangalore, India. The idea is to automatically learn a set of features from, potentially noisy, raw data that can be useful in supervised learning tasks such as in computer vision and insurance. Using Bayesian regression and Bayesian convolutional neural net on data from simulations of the Ising model - Kodemannen/Bayesian-Inference-and-Machine-Learning Strictly speaking, Bayesian inference is not machine learning. Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Note: this event has already taken place.Dr. He has a PhD in Statistical Physics from Tata Institute of Fundamental Research in Mumbai, India and has done post doctoral research from Weizmann Institute of Science, Israel and Georgia Institute of Technology, USA. In this manner we avoid the manual process of handcrafted feature engineering by learning … Machine Learning and Bayesian Inference. A text on Bayesian inference.