Machine Learning

Introduction

• Usage of algorithms to derive predictive insights from data and make repeated decisions
• Scaling up business intelligence and decision making
• Forwards looking—compare with analytics (backwards looking)
  • Predictions/inferences
• A computer writes a program for another computer
  • Computer figures out best program by looking at examples

Vs AI

• AI
  • Discipline (c.f. Physics)
  • Abilities of computers to mimic human behaviours
• ML
  • Methodology
  • Generally refers to supervised and unsupervised learning

What Problems Can ML Solve?

• Things currently done via ‘rules’
• Scale, automate, personalise
• Not for problems were one set of rules fits all—generic
  • Personalisation—lots of data required

Best Practices

• Break problems down into multiple ML models
  • Avoid trap of building monolithic models/solutions
• Avoid Training-Serving Skew
• Avoid Common Pitfalls of Machine Learning
• Avoid Bias in Machine Learning

Concepts and Definitions

Term	Definition
Example/instance/observation	Row in data set
Label	Value to predict. Can be numeric (requires regression model) or categorical (requires logistic model)
Features	Columns which aren’t labels.
Feature engineering	Combine/transform features.
One-hot encoding	Transform categorical data to numeric. 1 or 0 to indicate whether category applies to the observation
Hyperparameters	User-defined parameter used to control the learning process.

Model Types

• Supervised learning—task driven, identifies a goal, labelled data
  • Classification—predict a category
  • Regression—predict a continuous number
• Unsupervised learning—data-driven, identifies a pattern, unlabelled data
  • Clustering
  • Association—identify underlying relationships
  • Dimensionality reduction—transform data from a high-dimensional space to a low-dimensional space, combine features

Model Architectures

• Convolutional Neural Networks (CNNs)—image classification, object detection, recommendation systems
• Recurrent Neural Networks (RNNs)—sequence modelling, next word prediction, translation, speech-to-text
• Sorting/clustering—anomaly detection, pattern recognition
• Generational Adversarial Networks (GANs)—anomaly detection, cyber security, self-driving cars, reinforced learning

Strategy

• Follow The Path to Machine Learning
• Starts as data strategy
• Start with manual data analysis
  • Fail fast, try new ideas in an agile way
  • Then move to ML
• Data collection is the longest/hardest part—most likely to fail
• If you can’t do analytics, you can’t do ML

ML Effort Allocation

• Expectation:
  • Optimizing ML algorithm is most significant effort—practitioners focus on latest ‘shiny’ thing
• Reality:
  • Data collection and building infrastructure often overlooked
  • By spending time collecting data, KPIs often become apparent—rather than relying on intuition