Learn about the application of machine learning using R in financial domain
• Understanding machine learning basics
• Apply the model on financial dataset
• Interpretation of the model
Who should attend?
• Participants who have completed basics in R
• Financial and risk analysts
• Fraud detection teams
• Quantitive researchers
• Econometrics teams
• Research teams
Course Content:
Day 1
Session 1
Statistical Learning
• Estimation of f
• Introduction to Supervised and Unsupervised learning
• Regression vs Classification
Linear Regression
• Simple Linear Regression
• Multiple Linear Regression.
Classification
• Logistic Regression
• Linear Discriminant Analysis.
The audience will be introduced to basics of machine learning and statistics that would be needed to interpret them. After this session the audience will be able to predict a value of an asset/product using variables that would have some influence on it. And the audience will be able to apply models to predict yes or no (if a client would default or not depending on his current properties).
Day 2
Session 2
Resampling Methods
• Cross-Validation
• Leave one out cross validation
• k-fold cross validation and Bias Variance Trade off
• Boot strap
Session 3
Non-Linear Regression
• Polynomial Regression
Tree- based Methods
• Decision Tree• Bagging, Random Forests, Boosting
Audience will be introduced to advance training and testing setup to understand if the created models are worth using in their analysis.
Day 3
Session 4
Support Vector Machines
• Maximal Margin Classifier
• Support Vector Classifier
• SVMs Duration and Convexity
Unsupervised Learning
• Principal Components Analysis
• Clustering Methods
Audience would learn in classification. Example would be looking at the current statistics of the company one can predict which rating it should have looking at the historical records of other companies. Also learn how to deal with the curse of dimensionality. Example how to deal if there are too variables used in predicting the model.
