Professional Training

Machine Learning and Deep Learning in Finance and Investments

Rcademy, Online (+6 locations)
Length
5 days
Price
1,875 GBP excl. VAT
Next course start
01 Apr - 05 Apr, 2024 (+13 start dates)
Course delivery
Classroom, Virtual Classroom
Length
5 days
Price
1,875 GBP excl. VAT
Next course start
01 Apr - 05 Apr, 2024 (+13 start dates)
Course delivery
Classroom, Virtual Classroom
Leave your details so the provider can get in touch

Course description

Modern world organisations require professionals and teams that can perfectly use complex and huge volumes of datasets and make informed choices by deriving great strategic insight from these datasets. Machine and deep learning have become a very important part of the decision-making process in the financial sector. It can be utilised to assess risks, streamline operations, discern choices, inform investment decisions, and design actionable plans. Machine learning skills can improve the performance of a financial professional dramatically.

Which areas can be improved by machine learning in the financial sector?

Machine learning has the potential to improve several areas in the financial sector, including:

  • Fraud detection and prevention
  • Credit scoring and risk management
  • Customer service and support
  • Algorithmic trading
  • Portfolio optimisation and asset management
  • Predictive modelling for financial forecasting
  • Anti-money laundering
  • Insurance claims processing

What are the most important techniques in the machine and deep learning?

In machine learning and deep learning, some of the most important techniques include:

  • Supervised learning: learning from labelled data to predict outcomes
  • Unsupervised learning: finding patterns in data without labelled outcomes
  • Reinforcement learning: learning from reward-based feedback
  • Convolutional Neural Networks (CNNs): deep learning techniques for image classification

These techniques are widely used in various applications, including natural language processing, computer vision, and financial modelling. The choice of technique depends on the problem being solved and the type of data available.

The Machine Learning and Deep Learning in Finance and Investments Training Course by Rcademy will teach key skills for using data to inform decisions and developing machine learning models that will be used in the financial sector. It will give you the skills to apply deep learning solutions and classical machine learning techniques to financial problems that otherwise prove impossible for human beings. It will thoroughly teach systematic methods to solve important solutions using information gathered from existing data to increase your investment value in the new data-driven world. This Rcademy course will combine both practical and theoretical skills that will position the participants in a better position to solve supervised and unsupervised learning tasks that are beneficial to the organisation. The skills learnt from this course apply to numerous fields requiring data-driven decisions. Therefore, having such skills will enhance professionalism and increase the participants’ value in the financial industry.

Upcoming start dates

Choose between 13 start dates

11 Dec - 15 Dec, 2023

  • Classroom
  • Bali

09 Dec - 13 Dec, 2024

  • Classroom
  • Bali

01 Apr - 05 Apr, 2024

  • Classroom
  • Barcelona

08 Jan - 12 Jan, 2024

  • Classroom
  • Chicago, Illinois

08 Jul - 12 Jul, 2024

  • Classroom
  • Nairobi

14 Oct - 01 Nov, 2024

  • Classroom
  • New York

11 Mar - 15 Mar, 2024

  • Classroom
  • Seoul

04 Mar - 08 Mar, 2024

  • Virtual Classroom
  • Online

11 Mar - 15 Mar, 2024

  • Virtual Classroom
  • Online

01 Apr - 12 Apr, 2024

  • Virtual Classroom
  • Online

23 Sep - 27 Sep, 2024

  • Virtual Classroom
  • Online

07 Oct - 11 Oct, 2024

  • Virtual Classroom
  • Online

04 Nov - 08 Nov, 2024

  • Virtual Classroom
  • Online

Suitability - Who should attend?

The objectives of The Machine Learning and Deep Learning in Finance and Investments Training Course by Rcademy are to enable participants to:

  • Learn how to use modern and cutting-edge research in machine learning to make better models
  • Gain practical experience in creating predictive models using decision trees, neural networks, support vector classifiers, activation layer, and regression algorithm
  • Discover ways to implement, describe, and list the main differences between the working of the DBSCAN clustering algorithm and k-means clustering
  • Understand how and why predictive models fail and how they can be improved by applying gradient boosting, hyper-parameter tuning, cross-validation, and many other techniques
  • Apply the learnt skills and knowledge to develop predictive models used in live trading and understand how the models are used for live trading
  • Master artificial intelligence techniques and packages critical for financial markets prediction
  • Understand how to build supervised and unsupervised models
  • Use classical machine learning techniques

Training Course Content

Module 1: Cross-Sectional Data and Machine Learning

  • Fraud detection using deep learning and machine learning
  • Cryptocurrencies
  • Big data and computations in finance
  • Prediction entropy
  • Robo-advisors in Fintech
  • Machine learning and predictions using neural networks
  • Statistical modelling vs machine learning

Module 2: Probabilistic Machine Learning Modelling

  • Frequentist inference from data
  • Sequential Bayesian updates
  • Bayesian vs Frequentist estimations
  • Predictions using Bayesian updates
  • The Beta distribution
  • Model selection process
  • Occam’s Razor
  • Bayesian inference
  • The Bias-variance tradeoff for estimators
  • Model averaging
  • Model selection
  • How do select models from many models
  • Maximum likelihood estimation
  • Online learning prediction
  • Hidden indicator variable representation of mixture models

Module 3: Introduction to Machine Learning and Deep Learning

  • Using machine learning as opposed to using statistics
  • Machine learning applications in predicting risks, credit risk, portfolio optimisation and key selection
  • Deep learning methods
  • Reinforcement learning
  • Supervised vs unsupervised learning
  • Data vendors and their contribution to financial machine learning
  • Fintech in machine learning
  • Alternative data
  • Big data
  • Fintech

Module 4: Supervised and Unsupervised Learning 

  • Cross-sectional data
  • Evaluating machinelearning algorithms
  • Time series analysis
  • Hierarchical clustering
  • Clustering techniques
  • Affinity propagation
  • K-means
  • Regression, neural networks
  • Distance measurement
  • Random forest

Module 5: Decision and Random Trees

  • Introduction
  • Regression trees
  • Forecasting bond returns using macroeconomic variables
  • Classification trees
  • Default prediction based on accountancy data
  • Issues common to classification and regression trees

Module 6: Sequential Learning

  • Introduction
  • Exponential smoothing
  • Stability in autoregressive processes
  • Stationarity
  • Partial autocorrelations
  • Autoregressive processes
  • Heteroscedasticity
  • Predicting events
  • Principal component projection
  • Convexity and inequality constraints
  • Back-propagation
  • Computational considerations
  • Composition with ReLU activation
  • Function approximation with deep learning

Module 7: Gaussian Processes Bayesian Regression

  • Computational properties of Gaussian and Bayesian regression
  • Gaussian processes in finance
  • Structure exploiting inference
  • Mesh-free GPs
  • Exercise
  • Case study and practical example

Module 8: Feedforward Neural Networks

  • Introduction
  • Probabilistic reasoning
  • VC dimension
  • Approximating with compositions of functions
  • Function approximation with deep learning
  • Geometric interpretation of feedforward networks
  • Training, testing, and validation
  • Model averaging via dropout
  • RNN memory using partial autocovariance
  • Generalised recurrent neural networks
  • Neural network exponential smoothing
  • ∝-RNNs
  • Long short-term memory
  • Convolutional neural networks
  • Autoencoders

Course delivery details

This Rcademy course will be taught using interactive and participative methods involving practical activities and exercises to enhance participants’ learning experience. The training methodology will entail presentations, group discussions, lecture notes, case studies, and examples

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Rcademy
Floor 9, Zoom Building, Marassi Drive, Business Bay, Dubai, UAE
128 City Road, London, United Kingodom
EC1V 2NX

Rcademy

Rcademy is a global training and consultation organisation set out to bridge the gap between you now and what you can be in the near future. We are facilitators of knowledge impartation. Our team of established and experienced training enthusiasts...

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