Event Agenda

Agenda

Day One

Monday 25 November 2019

8:30

Registration and refreshments

9:00

Building Blocks 

  • Basic Python
  • Python for Machine Learning
  • Math for Machine Learning

10:30

Morning break

11:00

Classification

  • K-Nearest Neighbour (KNN)
  • Support Vector Machine (SVM)
  • Gaussian Naïve Bayes
  • Decision Tree
  • Ensemble: Random Forest, AdaBoost
  • Use Case: Credit Score 

12:30

Lunch

1:30

Clustering

  • K-Means
  • Affinity Propagation
  • Mean Shift
  • Hierarchical Clustering
  • DBSCAN
  • Use Case: Enhanced Index Tracking

3:00

Afternoon break

3:30

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Non-negative matrix factorisation (NMF)
  • Linear Discriminant Analysis (LDA)
  • Use Case: Factor Investment

5:00

End of day one

Day Two

Tuesday 26 November 2019

8:30

Refreshments

9:00

Artificial Neural Network

  • Perceptron
  • Deep feedforward
  • Backpropagation and Gradient Descent
  • Regularisation for Deep Learning
  • Optimisation for training Deep Models

10:30

Morning break

11:00

Deep Learning

  • RNN and LSTM
  • CNN
  • Autoencoders
  • Use case: Time Series Data Quality

12:30

Lunch

1:30

Reinforcement Learning

  • Markov Decision Process
  • Q Learning 
  • SARSA
  • Other models – DQN, A3C
  • Use Case: Trade Strategy

3:00

Afternoon break

3:30

Natural Language Processing

  • Syntax
  • Semantics
  • Discourse
  • Speech
  • Use Case: Sentiment Analysis

5:00

End of course