Lecturer: Josh Bloom (UC Berkeley; Wise.io, Inc.)

Lecture Slides (PDF) here

View the IPython notebook here

1. What is machine learning?

   • Flavors and facets of machine learning
     • supervised, semi-supervised, clustering, ...
     • classification / regression
   • When to use it, when not to
   • scikit-learn
   • testing/validation sets, cross-validation
   • metrics: ROC, AUC, confusion matrix

2. Regression

   • Linear regression
   • kNN
   • random forest

   [breakout: predict quasar redshifts from photometric data]

3. Classification

   • SVM
   • random forest
   • deep learning

   [breakout: predict Star/Galaxy/QSO from photometric data]

4. Improving your models

   • hyperparameter optimization
     GridSearchCV
   • dealing with missing data
   • Feature selection / feature importance
   • feature engineering

   [breakout: redo Star/Galaxy/QSO from photometric data]

5. Considerations in getting into production

   • multicore / multimachine
   • scikit-learn pipelines
   • Bigdata machine learning: Graphlab, MLlib (Spark)

1. Make sure you have the latest version (0.15) of scikit-learn

   • conda update scikit-learn

2. Download some datasets locally

   • TBD

• Scikit-learn: Machine Learning in Python

• Josh's lectures on scikit-learn from his graduate seminar class:

  • Notebooks. Take a look at newsgroups.ipynb to see some natural language processsing.