Chartered Fintech Professional (CFtP) Level 1B Training Module 3: Artificial Intelligence and Machine Learning
- Analytics & Tech
- Finance & Investment
This module is conducted in-person.
3 days
Weekdays and Saturday (9am - 5pm)
Who Should Attend
- Young graduates or early-career professionals who are exploring a new career in Fintech and would like to undergo a structured and rigorous industry-relevant programme
- Professionals who are already working at the intersection of finance and technology and who wish to keep abreast with latest fintech knowledge, applications, and trends
- Finance or Tech professionals who are currently leading teams in related industries and would like to plug their knowledge gaps in finance and/ or technology
- Industry regulators from central monetary authorities, law enforcers of financial crime etc., who are seeking a systemic perspective on the disruptions to financial services and monetary system
PREREQUISITES
- 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
Overview
Module 3 provides an overview of artificial intelligence (AI), machine learning (ML), and deep learning (DL). It begins by defining AI and discussing its brief evolution. It then explains the relationship between AI, ML, and DL, and why AI has seen significant effectiveness and progress over the last decade.
The module will also cover the concepts of probability theory and information theory, which are essential for understanding ML. It then discusses the most important general principles of ML, such as supervised learning, unsupervised learning, and reinforcement learning, as well as applications of association analysis, hierarchical clustering, K-means, and principal component analysis (PCA). These are all ML techniques that can be used to extract patterns and insights from data. The concepts and applications of linear regression and logistic regression are also covered. These are two of the most important machine learning techniques for making predictions. Participants will learn how to construct linear regression and logistic regression models, and how to interpret the results of these models.
Learning Objectives
At the end of the 3-day module, participants will be able to:
- Understand the brief evolution of AI and the relationship between AI, ML, and DL
- Understand why AI has seen significant effectiveness and progress over the last decade
- Understand the concepts of probability theory and information theory
- Understand the concepts and applications of association analysis, Apriori algorithm, hierarchical and K-means clustering, principal component analysis (PCA)
- Understand the concepts, interpretation, estimation, and applications of linear regression and logistic regression
Assessment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of the course. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$981 (After SSG Funding 70%) |
$381 (After SSG Funding 70% |
$981 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
|
International Participant |
$3,270 (No Funding) |
$3,270 (No Funding) |
$3,270 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Company Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees will be able to enjoy ETSS funding only if the company's SME's status has been approved. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Intake Information
This module is conducted in-person.
| Course | Dates |
|---|---|
| INTAKE | [To Be Confirmed] |
*Online registration will close 5 calendar days before the course start date
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