[introductory] An Introductory Course on Machine Learning and Deep Learning with Mathematica/Wolfram Language
An introductory course on Machine Learning and Deep Learning using Mathematica and the Wolfram Language.
I will start with a basic introduction of Mathematica and the Wolfram Language, demonstrating many of its broad range of capabilities. Then I will dive into some examples showing how to use traditional machine learning techniques using the Auto-ML features in the language. Finally, I’ll focus on specific examples where we will build, train, and deploy deep learning models based on different types of neural networks, especially on images and time-series data.
There will be hands-on sessions where the audience can write code and apply the techniques discussed in the course to solve several machine learning tasks using realistic datasets.
Some programming experience in any language and a basic understanding of machine learning and deep learning.
It is recommended to go through this quick introduction to the Wolfram Language before the course: https://www.wolfram.com/language/fast-introduction-for-programmers/en/
Daniel is a Vice President, Applied AI Lead at JPMorgan Chase specializing in deep learning for time-series. Previously, he was an AI Research Scientist working on moonshots at Google X. He completed his PhD in Astrophysics, with a fellowship in Computational Science and Engineering, at the University of Illinois at Urbana-Champaign and his Bachelor’s degree in Engineering Physics with Honors from IIT Bombay.
He pioneered the application of deep learning to detect gravitational waves from black holes at the National Center for Supercomputing Applications (NCSA) as a member of the LIGO collaboration. He also worked on HPC at Los Alamos National Laboratory and on deep learning/NLP at Wolfram (Mathematica, WolframAlpha). He has won the ACM Student Research Competition, the LSST Data Science Fellowship, and the NVIDIA Fellowship. His long-term interests lie in applying cutting-edge computer science and technology, especially AI, to accelerate scientific discoveries.