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Introduction to Advanced Machine Learning
It has become quite common these days to hear people refer to modern machine learning systems as “black boxes” - data goes in, decisions come out, but the processes between input and output are disconnected. This series is aimed at teaching the core concepts of machine learning by looking into the “black box” and understanding the math behind an algorithm. Popular Python packages will be used to implement these machine learning models on interesting datasets.

Join us for a 6-part technical series introducing some advanced machine learning concepts which encapsulate unsupervised machine learning problems and techniques to with unstructured data like text and sequential datasets. The webinar will be for 2 hours on Saturdays from 12 - 2pm PST (Check your local time here: https://www.worldtimebuddy.com/). The focus for the first hour will be the concept, formulae and statistics behind the algorithm . In the second hour, the goal would be to implement what has been learned using Python (source code will be provided).

★ Leaders: Aryan Gulati, Funke Olaleye, Joseph Itopa A., Rishika Singh, Sneha Thanasekaran, Sumana Ravikrishnan

❶ Sep 5,2020: k Nearest Neighbors
❷ Sep 12,2020: Clustering
❸ Sep 19,2020: Outlier Detection
❹ Sep 26, 2020: Text Data
❺ Oct 3, 2020: Time Series
❻ Oct 10, 2020: Neural Networks


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