agenda

agenda

Jesús Calderón

Managing director

Maclear Data Solutions

Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in  banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.

Machine Learning for Quantitative Finance | 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