**On hiatus Spring 2019**
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? Would you like to lead a coding walkthrough? 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.
In Fall 2018 the focus will be on unsupervised methods:
- September 5: Principal Component Analysis
- September 19: k-means clustering
- October 3: hierarchical clustering
- October 17: Medoid partitioning
- October 31: tSNE
- November 14: UMAP
- November 28: TextXD / Latent Class Analysis (?)
- December 12: Lightning talks
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
