Machine Learning Working Group, Fall 2018

We meet on alternating Wednesdays from 3-5pm at D-Lab (Barrows 356). We have no expectation of prior machine learning experience, and simply go through one algorithm a meeting, with about 30 minutes each in R & Python. We also incorporate lightning talks and other guest presentations throughout our meetings.

Fall 2018 - unsupervised methods

• Sept. 5: Principal component analysis (PCA)
• Sept. 19: K-means clustering
• Oct. 3: Hierarchical clustering
• Oct. 17: Medoid partitioning
• Oct. 31: tSNE
• Nov. 14: UMAP
• Dec. 12: Lightning talks

We are always looking for student/staff/faculty presenters. Please contact us if you are interested!

More information on the D-Lab MLWG website

Previous Semesters

• Spring 2018
  • k-nearest neighbors
  • decision tree
  • random forest
  • gradient boosting
  • elastic net
• Fall 2017
  • basics of neural networks for image processing
• Spring 2017
  • k-nearest neighbors
  • stepwise regression
  • linear and polynomial regression, smoothing splines
  • multivariate adaptive regression splines and generalized additive models
  • support vector machines
  • neural networks.
• Fall 2016
  • decision trees, random forests, penalized regression, and boosting

Resources

Books:

1. Intro to Statistical Learning by James et al. (free pdf) (Amazon)
2. Applied Predictive Modeling by Max Kuhn (Amazon)
3. Python Data Science Handbook by Jake VanderPlas (online version)
4. Elements of Statistical Learning by Hastie et al. (free pdf) (Amazon)
5. Modern Multivariate Statistical Techniques by Alan Izenman (Amazon)
6. Differential Equations and Linear Algebra by Stephen Goode and Scott Annin (Amazon)
7. Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, and Martin Wainwright (free pdf) (Amazon) and

Help:

• Getting Help with R
• Stack Overflow
• Quick-R
• R-Bloggers

Courses at Berkeley:

• Stat 154 - Statistical Learning
• CS 189 / CS 289A - Machine Learning
• COMPSCI x460 - Practical Machine Learning with R [UC Berkeley Extension]
• PH 252D - Causal Inference
• PH 295 - Big Data
• PH 295 - Targeted Learning for Biomedical Big Data

Online classes:

• Tibshirani and Hastie's Statistical Learning Free Course
• Coursera Data Science Specialization
• edX - Principles of Machine Learning
• edX - Applied Machine Learning
• Coursera - Machine Learning

Other Campus Groups:

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