Course curriculum

  • 1

    Getting Started

  • 2

    Overview

  • 3

    Chapter 0: Introduction

    • 0.1 Defining Machine Learning

  • 4

    Chapter 1: Foundations in Machine Learning

    • 1.0 Background and Resources

    • 1.1 Problem Statement

    • 1.2 Data Collection

    • 1.3 Data Cleaning

    • 1.4 Data Management

    • 1.5 Model Building

    • 1.6 Model Validation

    • 1.7 Reporting

    • 1.8 Monitoring and Version Control

    • 1.9 Overarching Components of the Pipeline

  • 5

    Chapter 2: Machine Learning Reference Matrix

    • Machine Learning Reference Matrix

  • 6

    Chapter 3: Case Studies

    • 3.1 End-to-End Lapse Analysis Case Study

    • 3.2.1 Introduction to Mortality Forecasting using Deep Learning Case Study

    • 3.2.2 Data Preparation

    • 3.2.3 Theoretical Background

    • 3.2.4 Fitting an LSTM using keras and tensorflow

    • 3.2.5 Further Advances when Fitting an LSTM using keras and tensorflow

    • 3.2.6 LSTM Notebooks, Lee-Carter & Cairns-Blake-Dowd Packages, and Dataset

  • 7

    Appendix and Further Resources

    • Extra Information

    • Some Commonly Used Regression and Forecasting Error Measures

    • ML_refList