Scaling to Big Data
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In machine learning, models are developed to represent and make predictions based on data. The model starts with random parameters and must “learn” these parameters by using historical data. In this class, we will discuss how learning is performed in practice, which is one of the key technical areas of machine learning. One of the big challenges in modern data science problems is the ability to perform such learning with massive datasets, so called “big data,” with minimal human intervention. This lecture will include discussion of back-propagation, variants of stochastic gradient descent, and adaptive gradient methods.
This lecture assumes background knowledge that would be acquired either from the “Neural Network Basics” IPLE or Weeks 1 and 2 of the Duke Coursera Course “Introduction to Machine Learning.”
|Date||Tuesday, October 1st, 2019|
|Time||4:30pm - 6:30pm|
|Location||Schiciano Auditorium, Side B|
|Enrolled||37 of 100|