Fintech - Part 4

Fintech - Part 4

Overview

This eCourse consists of two modules. Module 1 provides a high-level overview of unsupervised machine learning (U-ML) and highlights key factors to consider when using U-ML to solve business problems. When faced with large, unstructured, and unlabeled datasets, many companies use U-ML to discover patterns and identify previously unknown factors that may drive business outcomes. While U-ML is a potentially powerful tool, it is important to understand its limitations and the relative advantages of different approaches to it.

Module 2 provides an overview of neural networks (neural nets), deep learning, and reinforcement learning. Supervised and unsupervised machine learning (ML) use simple mathematical and statistical tools to process data and produce outputs that can help guide decision-making. They are relatively modest tools compared to the range of human intelligence. To develop more complex forms of artificial intelligence (AI), computer scientists and programmers have created artificial neural nets intended to mimic the functioning of the human brain. These neural nets have led to major advances in deep learning and reinforcement learning, enabling the creation of ever-more sophisticated AI tools.

Objective

On completion of this course, you will be able to:
- Define unsupervised machine learning and recall the best approach to using it effectively
- Define cluster analysis and compare the processes, uses, and limitations of hierarchical and non-hierarchical clustering
- Define dimension reduction and identify its uses and limitations
- Define deep learning and compare it to supervised and unsupervised machine learning
- Identify the key characteristics of artificial neural networks (ANNs) and compare them to the human brain
- List the limitations of ANNs and recall how these are overcome by recurring neural networks (RNNs) and convolutional neural networks (CNNs)
- Define reinforcement learning (RL) and its approach to problem-solving

Content

Module 1 - Unsupervised machine learning
Topic 1: Unsupervised ML Overview
Topic 2: Cluster Analysis
Topic 3: Dimension Reduction

Module 2 - Reinforcement Machine Learning & Neural Nets
Topic 1: Advanced AI Overview
Topic 2: Deep Learning & Neural Nets
Topic 3: Reinforcement Learning (RL)

Details

Code
TEPFT21001001
Venue
ePlatform
Language
English
Level
Intermediate
Hours
SFC:1.50, PWMA:1.50
Fees
All Member: HK$435
Staff of Corporate Member: HK$435
Non-Member: HK$615
Please choose the language
ENG