Class 5: Quantitative Legal Prediction + Data Driven Future of Law Practice- in this class we review the rapidly developing field of data driven law practice


Daniel Martin Katz   <CV> <SSRN> <arXiv>
Michael J. Bommarito  <CV> <SSRN> <arXiv>

Course Schedule
0.   Review Materials (Intro to Stats, Regression, etc.)
1.   Introduction to Legal Analytics
2.   Introduction to Machine Learning for Lawyers
3.   R Tutorials <Install> <Part1> <Part2> <Bonus>
4.   Github and RMarkdown Tutorial
5.   QLP + Data Driven Law Practice
6.   Overfitting, Underfitting, & Cross-Validation
7.   Binary Classification w/ Decision Tree Learning
8.   Ensemble Models including Random Forests
9.   Clustering (K-Means & Hierarchical Clustering)
10.  Data Visualization and DataViz in R
11.  Network Analysis and Law
12.  Data Preprocessing and Cleaning using dPlyR
13.  Natural Language Processing (NLP) Overview
14.  Applied Legal Analytics - NLP on Contracts
15.  Applied Legal Analytics - Judicial Prediction
16.  Advanced Topics - Lasso, Ridge Regression
17.  Advanced Topics - Kernels & SVM
18.  Advanced Topics - EM Algorithm  
19.  Advanced Topics - KNN + Naive Bayes
20.  Advanced Topics - Neural Networks
21.  Advanced Topics - Intro to Deep Learning
22.  Intro to Machine Learning as a Service (MLaaS)
23.  MLaaS and the Shifting Economics of #BigData