Key Takeaways
At the end of the course, the participants will be able to:
- Explain interpretable machine learning models
- Explain black-box machine learning models
- Adopt responsible AI practices by following the principles of fairness, ethics, accountability, explainability, privacy, security, and governance
- Understand current industry practices in Explainable AI
Who Should Attend
This course is suitable for participants working in banking/finance, regulatory sector.
- Data Analyst
- Data Scientist
- AI Expert
- Regulators
- Data engineers
- AI product managers
- Data science project managers
- underwriters
- Pricing Actuaries
- Head of claims
- Business intelligence analysts
- Chief data officers / Chief Information Officers/ Chief Information Security Officers
- Data protection officers
- Database admins
- Data stewards
Pre-requisites
- Good understanding of AI/ML
- Working knowledge in the finance sector would be a plus
- Experience and involvement in AI/ML projects in the finance sector would be a plus
What to Bring
No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.
If you are bringing a laptop, please see below for the tech specs:
|
|
Minimum
|
Recommended
|
|
Operating Systems
|
• Windows 7, 8, 10 or
• Mac OS
|
Laptop running the latest
version of either Windows or
Mac OS
|
|
System Type
|
32-bit
|
64-bit
|
|
Memory
|
8 GB RAM
|
16+ GB RAM
|
|
Hard Drive
|
256 GB disk size
|
|
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Others
|
• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) |
DirectX 10 graphics card for graphics hardware acceleration
|
Additional Software Requirements:
• MS Office
• Google Colab
What Will Be Covered
This course will cover:
- Introduction to explainable AI (XAI)
- Interpretable models (white box models)
- Black-box models: Model agnostic methods
- Black-box models: Model specific methods and Example based methods
- Python implementation
- Fairness in AI/ML
- Accountability in AI/ML
- Security, Privacy and Governance in AI/ML
Fees & Subsidies
Fees for 2024
| |
Full Fee |
Singaporeans & PRs
(self-sponsored) |
| Full course fee |
S$2700 |
S$2700 |
| ISS Subsidy |
- |
(S$270) |
| Nett course fee |
S$2700 |
S$2430 |
| 9% GST on nett course fee |
S$243 |
S$218.70 |
| Total nett course fee payable, including GST |
S$2943 |
S$2648.70 |
Note:
- All fees and subsidies are valid from January 2024, unless otherwise advised.
- All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
- From 1st January 2024, the GST will be increased to 9%.
Certificate
Certificate of Completion
Participants must meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.
Assessment Plan
This course aims to assess the participants’ technical skills & competencies will be assessed through the following means.
|
Type of assessments activity
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Supporting materials
|
Assessed output
|
Assessment method (Individual)
|
|
Individual workshop
The course participants will carry out technical workshop with a given dataset/business case.
|
Workshop Handouts
|
Completed workshop outputs as an Individual
|
Workshop output graded on a “Pass / Fail” basis by ISS assessor
|
|
Individual written assessment
An individual written assessment that will test the understanding of key concepts/ skills / technical competencies
|
Assessment Paper/ online quiz
|
Completed answer book/ online quiz
|
Answer book / online quiz graded on a “Pass / Fail” basis by ISS assessor
|
Assessors will be domain experts from the ISS Teaching Staff (Instructors for this course).
To be deemed ‘Competent’ for each learning objective (skills & knowledge), participants must have achieved a mark of ‘Competent’ in all the corresponding assessed activities.
TSC Description: Design and build intelligent machine reasoning systems that can integrate, make sense of, and act upon heterogeneous sensory information sources, using domain knowledge accumulated in respective industries.
TSC Proficiency Description: Level 4
Build knowledge-based intelligent software applications using machine reasoning techniques and computer programming.
- Knowledge
- Machine reasoning applications and technology
- Core machine reasoning techniques
- Components and techniques in knowledge-based systems\
- Reasoning system architectures
- Requirements and explainability for machine learning systems
- Types and sources of uncertainty and certainty factor technique
- Contemporary machine reasoning systems
- AI Ethics
- Abilities
- Analyse the business drivers and main application areas of machine reasoning
- Analyse reasoning systems for problem solving
- Analyse the forms to organise and represent knowledge, business rules and natural language
- Analyse techniques to draw new conclusions based on existing knowledge rules and new facts
- Analyse characteristics and results evaluation of advanced computational deductive reasoning techniques
- Examine uncertainty issues in machine learning
- Analyse characteristics and results evaluation of uncertainty handling techniques
- Apply logical inference to deduce new conclusions
- Evaluate performance of advanced mathematical models, inductive and deductive reasoning techniques
- Design and create reasoning systems
Join Us
Register now to master how to build Explainable and Responsible AI models for Finance
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.