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Machine Learning for Lawyers (Understanding)

Upcoming Classes

Ideal for small teams and individuals

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Overview

Machine Learning and AI are the next big wave in technology. And yet, one does not have to be a technical expert to understand the inputs and outputs of ML.

This course is intended for lawyers, legal support, and IT. Its goal is to explain the capabilities and limitations of ML. No coding is required, but the labs and exercises will assure that the attendants will grasp and retain the major principles and ideas.

The course maintains an optimal balance of theory and practice. For each machine learning concept, we first discuss the foundations, its applicability, and limitations. Then we explain the implementation and use, and specific use cases. The course consists of 50% lecture, 50% lab work.

Objectives

  • Attain thorough understanding of popular machine learning algorithms, their applicability, and limitations
  • Practice the application of these methods with the tools of choice
  • Achieve clarity in the real-world use of machine learning by illustrating each method with practical use cases

Audience

Lawyers, legal support, IT.

Duration

1 day

Pre-requisites

  • computer literacy, such as using MS Office

What to Bring

  • A reasonably modern laptop (Need to be able to connect to clusters running on cloud services; corporate laptops with overly restrictive firewalls usually give problems).
  • Working installed tools will be provided in the cloud. Students would only need a browser and optional use of a remote client.
  • Chrome browser with Markdown Preview Plus plugin

Detailed Outline

  1. Introduction
    • Goals and capabilities of Machine Learning
    • Machine learning: goals, types, results
    • What is meant by “training the model”?
  2. Classification
    • Classification and clustering
    • Document classification
    • Lab: Building document classifier
  3. Logistic Regression
    • How does it work, what does it give?
    • Predicting probabilities of successful outcomes
    • Lab: Building case outcome prediction model
  4. Linear regression
    • How does it work, what does it give?
    • Analyzing your business
    • Lab: predicting the size of the case
  5. Naive Bayes
    • How does it work, what does it give?
    • Filtering out irrelevant documents
    • Lab: spam filtering
  6. Decision Trees
    • How does it work, what does it give?
    • How to answer a few ‘yes’ or ‘no’ questions to arrive at a decision
    • Lab: predicting case outcome
  7. LDA
    • How does it work, what does it give?
    • Topic modeling with LDA
    • Lab: find major concerns in the document set
  8. Deep Learning
    • How does it work, what does it give?
    • Use case: machine translation
    • Lab: use document understanding model