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!
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