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When & Where
Alternating Wednesdays, 3-5PM, first meeting on January 31
Barrows 356: Convening Room

Are you new to machine learning but do not know how to get started? Do you have experience with machine learning and are looking for a venue to practice presenting your research? If you answered yes to either of these questions, then come join the UC Berkeley D-Lab Machine Learning Working Group! 

This brown-bag series will introduce you to central themes in the form of workshop-style coding walkthroughs using R and Python.

The tentative schedule is as follows:

January 31: k-nearest neighbor

February 14: decision tree

February 28: random forest

March 14: lightning talks

April 11: gradient boosting

April 25: elastic net

May 9: lightning talks

You are invited to present lightning talks on your individual research on March 14 and May 9. We will focus on key frameworks in R such as caret and SuperLearner and in Python like scikit-learn, tensorflow, and keras. 

We also encourage you to bring topics for discussion that focus on a variety of themes including data cleaning, visualization, automation, cloud computing, and parallel processing. 

Prior knowledge: R FUN!damentals: Parts 1 through 3 or previous intermediate working knowledge of R or Python FUN!damentals and previous work with NumPy and SciPy are recommended. 

Click here download install R

Click here to download RStudio Desktop Open Source License FREE

Click here to download Python Anaconda Distribution

Visit our GitHub repo at: https://github.com/dlab-berkeley/MachineLearningWG

D-lab Facilitator: 
Evan Muzzall
Participant Technology Requirement: