Event Agenda

Agenda

Day One: Monday November 25, 2019

8:30

Registration and refreshments

9:00

Building blocks 

  • Basic python
  • Python for machine learning
  • Math for machine learning

10:30

Morning break

10:45

Classification

  • K-Nearest Neighbour (KNN)
  • Support Vector Machine (SVM)
  • Gaussian naïve bayes
  • Decision tree
  • Ensemble: Random Forest, AdaBoost
  • Use case: credit score 

12:00

Lunch

1:00

Clustering

  • K-Means
  • Affinity propagation
  • Mean shift
  • Hierarchical clustering
  • DBSCAN
  • Use case: Enhanced index tracking

2:30

Afternoon break

3:00

Dimensionality reduction

  • Principal Component Analysis (PCA)
  • Non-negative matrix factorisation (NMF)
  • Linear Discriminant Analysis (LDA)
  • Use case: factor investment

4:30

End of day one

Day Two: Tuesday November 26, 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

10:45

Deep learning

  • RNN and LSTM
  • CNN
  • Autoencoders
  • Use case: time series data quality

12:00

Lunch

1:00

Reinforcement learning

  • Markov decision process
  • Q learning 
  • SARSA
  • Other models – DQN, A3C
  • Use case: trade strategy

2:30

Afternoon break

3:00

Natural language processing

  • Syntax
  • Semantics
  • Discourse
  • Speech
  • Use case: sentiment analysis

4:30

End of course