INTRO
- setup (installing python & scientific libraries)
- intro to DS/ML/data exploration with UNIX
- logistic regression
- intro to ML theory (train/test/OOS, cv, overfitting, etc)
- regularization, bias/variance tradeoff, sample complexity, VC dimension
MODELS
- kNN, greedy algos
- probability, classical statistics, naive Bayes
- decision trees & random forests
- ensemble methods
- clustering
- svm's
ETC
- visualization w/ d3
- intro to nosql
- map-reduce
- dimensionality reduction
- recommender systems
- site visit?
PROJECTS
- working session
- presentations I
- presentations II
- (open, see below)
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addl (1/2 lectures):
- project progress check
- industry stuff
- 3x guest speakers
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possible wkd sessions:
- pandas
- open data (incl apis)