Machine Learning for Banking (with R) Training Course

Introduction

• Difference between statistical learning (statistical analysis) and machine learning
• Adoption of machine learning technology by finance and banking companies

Different Types of Machine Learning

• Supervised learning vs unsupervised learning
• Iteration and evaluation
• Bias-variance trade-off
• Combining supervised and unsupervised learning (semi-supervised learning)

Machine Learning Languages and Toolsets

• Open source vs proprietary systems and software
• R vs Python vs Matlab
• Libraries and frameworks

Machine Learning Case Studies

• Consumer data and big data
• Assessing risk in consumer and business lending
• Improving customer service through sentiment analysis
• Detecting identity fraud, billing fraud and money laundering

Introduction to R

• Installing the RStudio IDE
• Loading R packages
• Data structures
• Vectors
• Factors
• Lists
• Data Frames
• Matrixes and Arrays

How to Load Machine Learning Data

• Databases, data warehouses and streaming data
• Distributed storage and processing with Hadoop and Spark
• Importing data from a database
• Importing data from Excel and CSV

Modeling Business Decisions with Supervised Learning

• Classifying your data (classification)
• Using regression analysis to predict outcome
• Choosing from available machine learning algorithms
• Understanding decision tree algorithms
• Understanding random forest algorithms
• Model evaluation
• Exercise

Regression Analysis

• Linear regression
• Generalizations and Nonlinearity
• Exercise

Classification

• Bayesian refresher
• Naive Bayes
• Logistic regression
• K-Nearest neighbors
• Exercise

Hands-on: Building an Estimation Model

• Assessing lending risk based on customer type and history

Evaluating the performance of Machine Learning Algorithms

• Cross-validation and resampling
• Bootstrap aggregation (bagging)
• Exercise

Modeling Business Decisions with Unsupervised Learning

• When sample data sets are not available
• K-means clustering
• Challenges of unsupervised learning
• Beyond K-means
• Bayes networks and Markov Hidden Models
• Exercise

Hands-on: Building a Recommendation System

• Analyzing past customer behavior to improve new service offerings

Extending your company's capabilities

• Developing models in the cloud
• Accelerating machine learning with additional GPUs
• Applying Deep Learning neural networks for computer vision, voice recognition and text analysis

Closing Remarks