Module 5 - Data Science & Machine Learning ll

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In module five, you will learn several more methods used for machine learning in finance. Starting with unsupervised learning, deep learning and neural networks, we will move into natural language processing and reinforcement learning. You will study the theoretical framework, but more importantly, analyze practical case studies exploring how these techniques are used within finance.

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Sections

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Unsupervised Learning |

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  • K Means Clustering
  • Self Organizing Maps
  • Strengths and Weaknesses of HAC and SOM
  • Applications in Finance

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Unsupervised Learning II

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  • The curse of dimensionality
  • t-distributed Stochastic Neighbor Embedding (t-SNE)
  • Uniform Manifold Approximation and Projection (UMAP)
  • Autoencoders
  • Applications in Finance

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Deep Learning & Neural Networks

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  • What are Artificial Neural Networks and Deep Learning?
  • Perceptron Model, Backpropagation 
  • Neural Network Architectures: Feedforward, Recurrent, Long Short Term Memory, Convolutional, Generative adversarial
  • Applications in Finance

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Natural Language Processing

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  • Pre Processing
  • Word vectorizations, Word2Vec
  • Deep Learning & NLP Tools
  • Application in Finance: sentiment change vs forward returns; S&P 500 trends in sentiment change; Earnings calls analysis.
  • Code examples

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Reinforcement Learning I

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  • Recap of multi-armed bandit
  • The exploitation-exploration trade-off
  • Exploration strategies: softmax versus epsilon-greedy
  • Risk-sensitivity in Reinforcement-learning

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Reinforcement Learning II

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  • Practical Machine Learning

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AI Based Algo Trading Strategies

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  • Basic Financial Data Analysis with Python and Pandas
  • Creating Features and Label Data from Financial Time Series for Market Prediction
  • Application of Classification Algorithms from Machine Learning to Predict Market Movements
  • Vectorized Backtesting of Algorithmic Trading Strategies based on the Predictions
  • Risk Analysis for the Algorithmic Trading Strategies

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Practical Machine Learning Case Studies for Finance

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  • Asset Price Behaviour and Volatility modeling
  • Empirical SDEs with estimated drift and diffusion functions
  • Generalized Stoch Vol models, learning dynamical models from data
  • Option pricing and hedging using ML
  • Model free pricing of exotic options
  • Robust Portfolio Optimisation with ML
  • Denoising & Detoning covariance matrices
  • Nested Cluster Optimization

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Lecture order and content may occasionally change due to circumstances beyond our control; however this will never affect the quality of the program.

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Data Science & Machine Learning l
Fixed Income & Credit