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 Social Sciences Data Laboratory (D-Lab) Machine Learning Working Group!
This brown-bag series will introduce you to central themes in the form of short lectures and topic discussions. Coding walkthroughs will be demonstrated using R and Python and will focus on providing basic understandings of gradient boosting, principal component analysis, lasso regression, and random forest model creation and visualization, neural networks, as well as key frameworks in R like caret, SuperLearner, and h2o.ai and in Python like scikit-learn, tensorflow, theano, and keras.
We also encourage students to bring topics for discussion that focus on a variety of themes including algorithm creation, data cleaning, visualization, automation, cloud computing, and parallel processing.
Prior knowledge: R for Data Science: Parts 1 through 4 or previous intermediate working knowledge of R. Python introductory series and previous work with NumPy and SciPy.