Fridays, 12-1pm in 356 Barrows Hall

Fall 2016 Schedule

• September 23 - Introductory meeting
• October 7 - Decision trees
• October 21 - Random forests
• November 4 - Penalized regression - lasso, ridge, elastic net
• November 18 - Evan's skull dataset and GBM
• December 2

Spring 2017 Schedule - to be determined, topics welcome!

More information on the D-Lab website

Books:

• Intro to Statistical Learning (free pdf) (Amazon page) by Gareth James et al.
• Applied Predictive Modeling by Max Kuhn
• Elements of Statistical Learning
• Many others (any recommendations?)

Courses at Berkeley:

• Stat 154 - Statistical Learning
• CS 189 / CS 289A - Machine Learning
• PH 252D - Causal Inference
• PH 295 - Big Data
• PH 295 - Targeted Learning for Biomedical Big Data
• INFO - TBD

Coursera and other online classes

• To add

D-Lab Machine Learning Trainings

• D-Lab - Intro to Machine Learning
• Erin LeDell - h2o.ai
• Rochelle Terman - scikit-learn

Specifics on the D-Lab calendar

Other Campus Groups

• Machine Learning @ Berkeley
• D-Lab's Cloud Computing Working Group
• D-Lab's Computational Text Analysis Working Group
• The Hacker Within / Berkeley Institute for Data Science