Introductory workshop on Computational Machine Learning
Submitted by Abhijith Chandraprabhu (@abhijithc) on Tuesday, 1 May 2018
Technical level: Intermediate
Abstract
You have been hearing about machine learning (ML) and artificial intelligence (AI) everywhere. You have heard about computers recognizing images, generating speech, natural language, and beating humans at Chess and Go. In this workshop, you will be learning the math and program the math of AI, for example we start by hand coding all the components of a neural network, without calling any libraries. The concepts will be taught in Julia, a modern language for numerical computing and machine learning. We will eventually use Flux, which is a Julia machine learning stack, written 100% in Julia, to build some deep learning applications like image recognition and language detection.
The takeway from this workshop is good understanding of the first principles of deep learning and ability to build elegant highperformance deep learning applications.
Outline
 Some Motivations for Machine Learning
 Data  Key to AI and ML
a. Computer Representation of Data  Modeling Data
a. Functions and parameters b. Data fitting by varying parameters c. Quantifying how far we are from the goal: the loss function  What is Learning
a. Motivation: Fitting Parameters By Hand b. “Learning by Nudging”: The Process of Descent¶  Introduction to Neurons
 Build neural networks
Requirements
 Not to shy away from getting into some mathematical concepts
 Commitment to strive towards understanding the concepts and program for applications
 Regular attendance and timely completion of assignments
 Active participation in the classes
 Commitment to follow on work or projects in order to apply the concepts in real life
Speaker bio
Abhijith is currently working as a data scientist at Julia Computing, Bangalore. Previous to that he worked as data scientist at Gramener, Hyderabad.
Comments


Sabrina Mosbi (@sabrinamosbi)
Interesting thanks for all the details, requierements are important.
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alenvable (@alenvable)
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The takeway from this workshop is good understanding of the first principles of deep learning and ability to build elegant highperformance deep learning applications.
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