Fintech

Fintech - Part 3

Overview

This eCourse consists of two modules on supervised machine learning (S-ML). Artificial intelligence systems can help solve business problems by answering questions and facilitating data-informed decisions. Machine learning (ML) programs – which fall into two categories, supervised and unsupervised – use raw data to develop models that can produce actionable business intelligence. Both supervised and unsupervised ML systems can thus support business processes and inform both day-to-day and strategic decisions.

S-ML systems are widely used across business sectors. While they can help facilitate faster and better decision-making, however, there are pitfalls associated with their use. It is, therefore, important to understand their limitations before deploying such systems. It is also necessary to understand what types of data are needed to build effective S-ML systems and how such systems use data to achieve their outputs.

Module 1 provides an overview of S-ML, including its advantages, limitations, and appropriate use cases.

Module 2 explores the use of S-ML systems in practice and the data and mathematics that underpin their outputs.

Objective

On completion of this course, you will be able to:
- Distinguish between supervised and unsupervised machine learning
- Define supervised machine learning and identify its limitations and use cases
- List the key factors to consider when using a supervised machine learning system to solve a problem
- Identify the types of data required to train a supervised machine learning system
- Recall the process used to train a supervised machine learning system, including the mathematical techniques used

Content

Module 1 - Supervised Machine Learning – An Introduction
Topic 1: Machine Learning Overview
Topic 2: Supervised Machine Learning (S-ML)

Module 2 - Supervised Machine Learning In Practice
Topic 1: Overview of Practical S-ML
Topic 2: Data & Methodology
Topic 3: Training an S-ML System

Details

Code
TEPFT21000901
Venue
ePlatform
Relevant Subject
Type 1 - Dealing in securities
Type 2 - Dealing in futures contracts
Type 3 - Leveraged foreign exchange trading
Type 4 - Advising on securities
Type 5 - Advising on futures contracts
Type 6 - Advising on corporate finance
Type 7 - Providing automated trading services
Type 8 - Securities margin financing
Type 9 - Asset management
Type 10 - Providing credit rating services
Type 11 - Dealing in OTC derivative products or advising on OTC derivative products
Type 12 - Providing client clearing services for OTC derivative transactions
Language
English
Level
Intermediate
Hours
SFC:1.50, PWMA:1.50
Fees
All Member: HK$460
Staff of Corporate Member: HK$460
Non-Member: HK$665