agenda

agenda

Machine Learning in Quant | Agenda

Agenda timing is in HKT/SGT

08:4509:00

Registration

09:00 - 10:00

09:0010:00

Introduction to Machine Learning and Financial Applications

09:00 - 10:00

  • From Probability to Statistics to Machine Learning (ML)
  • Core components of the ML process
  • Current applications of ML in Finance

10:0010:15

Break

09:00 - 10:00

10:1511:15

Supervised Learning Models

09:00 - 10:00

  • Naïve Bayes
  • Decision Trees
  • Ensemble Methods: Boosting, Bagging and Random Forest
  • Regression Models
  • Support Vector Machines
  • Neural Nets and Deep Learning

11:1511:15

End of Day 1

09:00 - 10:00

08:4509:00

Registration

09:00 - 10:00

09:0010:00

ML in Risk Management

09:00 - 10:00

  • Classification with Imbalanced Data
  • Choosing an Appropriate Performance Measure
  • Class Weights for Cost-Sensitive Training
  • Synthetic Sampling Methods
  • Feature Engineering

10:0010:15

Break

09:00 - 10:00

10:1511:15

Unsupervised Methods and Reinforcement Learning

09:00 - 10:00

  • Unsupervised learning
    • Dimensionality Reduction
    • Clustering
    • Topic Models
    • Autoencoders
  • Reinforcement Learning

11:1511:15

End of Day 2

09:00 - 10:00

08:4509:00

Registration

09:00 - 10:00

09:0010:00

Anomaly Detection

09:00 - 10:00

  • Anomalies, their types, and challenges in anomaly detection
  • Anomaly Detection Methods
    • k-Nearest Neighbours
    • Local Outlier Factor
    • Cluster-Based Local Outlier Factor
  • Isolation Forest

10:0010:15

Break

09:00 - 10:00

10:1511:15

Neural Nets and Deep Learning

09:00 - 10:00

  • Deep Learning and Complexity
  • NLP and Word Embeddings
  • Volatility Prediction with Neural Nets
  • Deep Learning and Options Pricing
  • Other Applications

11:1511:15

End of Day 3

09:00 - 10:00

08:4509:00

Registration

09:00 - 10:00

09:0010:00

Explainability in Machine Learning

09:00 - 10:00

  • Definitions of Explainability
  • Explainability in Finance and Financial Regulation
  • How to Achieve ML Explainability

10:0010:15

Break

09:00 - 10:00

10:1511:15

Regulatory Implications of Machine Learning

09:00 - 10:00

  • Adoption of ML
  • Applications of ML in Supervisory Activities
  • Evolving and Emerging Risks

11:1511:15

End of course

09:00 - 10:00