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)