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Applied Machine Learning and AI in Finance

Day One

Positioning of Machine Learning vs. Deep Learning

Machine Learning Introduction

  • Supervised vs. unsupervised
  • Association rules
  • Classification vs. regression problems
  • Cross validation and hyper parameter optimization

Unsupervised Learning

  • Clustering analysis

Workshop: Equity / credit models

  • Outlier detection
  • Distance Metrics in Sklearn

Workshop: Robust outlier detection

  • Kernel Density Estimation

Workshop: BitCoin-application

  • Hidden Markov Models

Workshop: GBPEUR-timeseries analysis

Supervised Learning

  • Regression with regularisation
    • Ridge regression
    • Lasso
    • Elastic Net

Workshop: Portfolio hedging

  • Miscellaneous Regression Techniques
    • Gaussian Process Regression (GPR)
    • Principal Component Regression (PCR)
    • Partial Least Squares (PLS)

Workshop: Volsurface smoothing

  • Classification
    • Naive Bayes classification: A straightforward and powerful technique to classify data
    • Linear Discriminant Analysis (LDA)
    • Logistic Regression

Workshop: Classification trees

Day Two

Natural Language Processing

  • Extracting real value from social media posts, images, email, PDFs and other sources of unstructured data is a big challenge for enterprises
  • Explore and tokenize a text
  • Sentiment analysis
  • Text Classification
  • Understanding concepts such as WordNet, Word2Vec, Stemming, etc.

Workshop: Sentiment analysis of tweets

Deep Learning (AI)

  • Deep Learning as a subfield of machine learning - Artificial Neural Networks (ANN) algorithms
  • Forward and backward propagation
  • Network topology
  • Tensorflow 2.0

Workshop: Regression, classification and time series forecast


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