Chartered Fintech Professional (CFtP) Level 2 Training Module 1: Artificial Intelligence, Machine Learning and Deep Learning in Finance
About This Course
Module 1 of the Level 2 training will present some advanced techniques, algorithms, and models to lift the magic veil of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). There are 3 sections to this module: The first section on “Machine Learning” presents various advanced supervised and unsupervised machine learning models and the underlying mechanism and rationale in model selection. This will equip participants with the essential knowledge to develop, evaluate, and interpret a machine learning model. The second section on “Deep Learning” presents a more in-depth discussion of various commonly used neural network architectures, including the artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and generative adversarial networks (GANs). Finally the third section Natural Language Processing (NLP) presents another rising hot topic in AI, namely natural language processing (NLP). This section will have a detailed discussion of some core NLP techniques and NLP applications in finance.
What You'll Learn
• Understand the bias-variance tradeoff, distinguish between training Mean Squared Error (MSE) and testing MSE and understand the concepts and relationships among flexibility, complexity, interpretability, generalization, and prediction precision
• Design subset selection methods for a given data set using the three methods introduced, discuss and compare the merits and drawbacks of different subset selection approaches
• Identify the differences among various evaluation criteria; appraise the advantages of shrinkage methods
• Understand the confusion matrix and compute sensitivity, specificity, type I error, type II error, and precision
• Understand the design of different cross-validation approaches
• Understand the architecture of Artificial Neural Network (ANN); the forward feed and backpropagation algorithms; different types of activation functions; how loss function and stochastic gradient descent optimise the learning process and distinguish between batch and epoch
• Understand the architecture of Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN)
• Understand the concept of Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) , identify and appraise the NLP applications in the real world & understand various discussed NLP techniques
Entry Requirements
No prerequisites, but participants are strongly encouraged to go through the assigned pre-reading materials and videos, especially if one does not have any prior learning or working knowledge in the subject matter of this Module.
If the participant intends to register for the Chartered Fintech Professional examination following the completion of this training course, do note that an undergraduate degree from a recognised university or equivalent professional qualification is a compulsory enrolment requirement.