Machine Learning for Managers

✍ Scribed by Paul Geertsema

• Publisher: Routledge
• Year: 2023
• Tongue: English
• Leaves: 181
• Category: Library

✦ Synopsis

Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math.

The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization.

This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Overview
Preface
List of Figures
List of Tables
Author
I Understanding Machine Learning
1 Let’s Jump Right in
1.1 What Can We Learn from 34 Lines of Code?
1.2 Fitting ML into the Big Picture
1.3 A Layered Perspective of Machine Learning
1.4 Data, Compute and Methods
1.5 ML Drives Wealth Creation
2 Different Kinds of ML
2.1 An Introduction to the ML Zoo
2.2 Supervised vs Unsupervised ML
2.3 Generative Learning
2.4 Reinforcement Learning
2.5 Online vs Batch Training
2.6 Value-Destroying vs Value-Creating ML
3 Creating ML Models
3.1 Data, Instances and Features
3.2 Targets and Inputs
3.3 Training, Validation and Test Data Sets
3.4 The Machine Learning Recipe
3.4.1 Specify the Problem
3.4.2 Collect the Data
3.4.3 Split the Data
3.4.4 Understand and Explore the Data
3.4.5 Pre-process the Data and Construct Features
3.4.6 Select A Machine Learning Approach
3.4.7 Select Hyper-Parameters
3.4.8 Train the Model
3.4.9 Evaluate the Model on Validation Data
3.4.10 If Validation Performance is Weak
3.4.11 Train the Final Model
3.4.12 Evaluate the Final Model on the Test Data
3.4.13 If Test Performance is Weak
3.4.14 Deploy the Model in Production
3.4.15 Monitor the Model
4 Linear Models
4.1 A Simple Linear Model
4.2 Training Linear Regression Models
4.3 Using Feature Transformations in Linear Models
4.4 Performance Measures for Regression Tasks
4.5 Linear Models with Indicator Variables and Interactions
4.6 Classification with Logistic Regression
4.7 Regularization – Ridge Regression, Lasso and Elastic Net
5 Neural Networks
5.1 A Brief History of Neural Networks
5.2 A Linear Model is a Neural Net (A Very Simple One)
5.3 All You Ever Wanted to Know About Nodes
5.4 More Complex Neural Networks
5.5 Training A Neural Network
5.6 The MNIST Example
5.7 A Peek into the Future – Transformers and Language Models
6 Tree-Based Approaches, Ensembles and Boosting
6.1 The Titanic Example
6.2 Making Predictions with A Tree Model
6.3 Performance Measures for Classification Tasks
6.3.1 Confusion Matrices
6.3.2 Classification Performance Measures
6.3.3 Thresholds and the ROC-AUC Measure
6.4 Ensembles and Random Forests
6.5 Gradient Boosting Machines
7 Dimensionality Reduction and Clustering
7.1 Why Reduce Dimensionality?
7.2 Principal Components Analysis
7.3 Clustering
7.3.1 K-Means Clustering
7.3.2 Hierarchical Agglomerative Clustering (HAC)
8 Unstructured Data
8.1 Images
8.2 Sequences
8.3 Transformers
9 Explainable AI
9.1 Why Do We Need Explainable AI?
9.2 Explaining Models
9.2.1 Intrinsically Interpretable Models
9.2.2 Surrogate Models
9.3 Explaining Predictions
II Managing Machine Learning Projects
10 The ML System Lifecycle
10.1 Context
10.2 Identify
10.3 Pilot
10.4 Pipeline
10.5 Development
10.6 Deployment and Monitoring
10.7 The Circle of Life
11 The Big Picture
11.1 Why Getting Things Done is Hard
11.2 Governance Model
11.3 Security and Privacy
11.4 Explain Ability and Fairness
11.5 Laws, Regulations and Compliance
12 Creating Value with ML
12.1 Sources of Value
12.2 The Data-Centric Firm
12.3 The Economics of Platforms
12.4 Outside of Platforms
13 Making the Business Case
13.1 Executive Summary
13.2 Description of the Project
13.3 Project Benefits
13.4 Proof-of-Concept
13.5 Required Resources
13.6 Technical Appendix
14 The ML Pipeline
14.1 Who Needs a Pipeline Anyway?
14.2 The ML Pipeline
15 Development
15.1 A Very Brief Introduction to Software Engineering
15.1.1 Divide and Conquer
15.1.2 Expose Interfaces, Hide Implementations
15.1.3 Implement Incrementally
15.1.4 Use Version Control
15.1.5 Conduct Automated Testing
15.2 Validating the Pipeline
15.2.1 Run-Through Data
15.2.2 Synthetic Data
15.2.3 Trivial Models
15.2.4 Simple Benchmark Models
15.2.5 Current Approach
15.3 Model Development
15.4 Performance vs Value
15.5 Technical Debt
16 Deployment and Monitoring
16.1 Set Up the Production Environment
16.2 Connect the Plumbing
16.3 Test, Test, Test
16.4 Flip the Switch
16.5 Continuous Monitoring
16.6 Final Thoughts
Index