This class is designed to train students to efficiently manage, collect, explore, analyze, and communicate in a legal profession that is increasingly being driven by data. 

Our goal is to imbue our students with the capability to understand the process of extracting actionable knowledge from data, to distinguish themselves in legal proceedings involving data or analysis, and assist in firm and in-house management, including billing, case forecasting, process improvement, resource management, and financial operations.  You will learn basic concepts in machine learning / data science and will receive an introduction to R (the open source programming language which is lingua franca of statistical computing).

This course assumes prior knowledge of statistics, such as might be obtained in Quantitative Methods for Lawyers or through advanced undergraduate curricula.  This class is not for everyone; for many, it will prove to be challenging.  With that warning, we encourage you to consider your interest and career aspirations against the unique experience and value of this class.  To our knowledge, this is the only existing class that teaches these quantitative skills (such as machine learning) to lawyers and law students. 

Still in beta - we will be adding much more to this page in the coming months!

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

Review Modules
I.     Review Materials (Intro to Stats, Regression, etc.)
II.    R Tutorials <Install> <Part1> <Part2> <Bonus>
III.   Github and RMarkdown Tutorial

Course Modules
1.    Introduction to Legal Analytics
2.    Introduction to Machine Learning for Lawyers
3.    Quantitative Legal Prediction + Business of Law
4.    Bias/Variance, Precision/Recall & Dimensionality
5.    Overfitting, Underfitting, & Cross-Validation
6.    Logistic Regression and Maximum Likelihood
7.    K Nearest Neighbors + Naive Bayes Classifiers
8.    Binary Classification w/ Decision Tree Learning
9.    Ensemble Models including Random Forests
10.  Clustering (K-Means & Hierarchical Clustering)
11.  Data Visualization and DataViz in R
12.  Data Preprocessing and Cleaning using dPlyR
13.  Network Analysis and Law
14.  Natural Language Processing (NLP) Overview
15.  Applied Legal Analytics - Contract Analytics
16.  Applied Legal Analytics - Exploring SEC Data
17.  Applied Legal Analytics - Judicial Prediction
18.  Applied Legal Analytics - Regulatory Outcomes
19.  Applied Legal Analytics - Sentiment Analysis
20.  Advanced Topics - Support Vector Machines
21.  Advanced Topics - EM Algorithm  
22.  Advanced Topics - Neural Networks
23.  MLaaS and Shifting Economics of #BigData