Course 805:
Machine Learning Part I: Supervised and Unsupervised Learning

(3 days)

 

Course Description

There are multiple frameworks that make implementing an understood Machine Learning (ML) algorithm relatively easy. What is needed is an understanding of ML and the specific use cases, capabilities, and limitations of the different algorithms. The primary objective of this course is to give the student the words and understanding necessary to apply the correct ML algorithms to a real-world problem and follow the current research to know when and where it can be applied to real-world problems. This course is long on understanding (the hard part) and short on math (the easy part) of ML. The tactic is to explore each algorithm using a real-world example to extract general knowledge about the approach.

Learning Objectives

  • Understand the context of Machine Learning (ML)
  • Understand the terminology used to talk about ML
  • Understand the limitation of different approaches
  • Understand the advantages of different approaches
  • Understand the business application of ML
  • Understand the different business use cases of ML
  • Be introduced to an approach for working with ML

Prerequisites

A curiosity about Machine Learning and a desire to learn how to apply it to business and organizational problems.


Course Outline

 

Unit 1: Introduction

  • What Is Learning?
  • What Is Machine Learning?
  • Aims of Machine Learning
  • What Machine Learning Can Currently Do
  • Classification of ML
  • Relationship to Other Technologies
  • Business Uses of Machine Learning
  • Discussion Business Uses

Unit 2: An Approach to Using ML

  • The Steps to Take
  • Data Problems
  • Use Case Workshop

Unit 3: Unsupervised Learning

  • Hierarchical Clustering
  • K-Means
  • Hidden Markov
  • Anomaly Detection
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition

Unit 4: Supervised Learning

  • Naïve Bayesian
  • Decision Trees
  • Random Forest
  • Linear Regression
  • Classification Regression
  • Support Vector Machines
  • Ensembles

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