Advanced Machine Learning for Financial Services

Key Takeaways

Upon completion of this 4-day Advanced Machine Learning for Financial Services course, attendees will learn how to (in the context of finance sector):

• Analyse and select the type of algorithms for their business problems.
• Implement and evaluate both ensemble and deep learning methods for real world financial applications.
• Evaluate the architectural considerations for deployment and monitoring.
• Analyse the role of MLOps and the role of responsible & explainable AI in financial services along with some practical examples.

Who Should Attend

This course has been currently designed to be suitable for participants working in banking/finance, regulatory sector and performing following roles: 

• Data Analyst
• Financial analysts
• Data Scientist
• AI Expert
• Regulators
• Data engineers
• AI product managers
• Data science project managers
• Underwriters
• Pricing Actuaries
• Head of claims
• Business intelligence analysts

Pre-requisites:

• Working knowledge in the finance sector
• Good understanding of AI/ML
• Experience and involved in AI/ML projects in the finance sector
• Comfortable with programming in Python and PyTorch
• Comfortable with using Google Colab notebooks

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:

What Will Be Covered

This course will cover:

• Introduction to ensemble methods and their applications in financial domain.
• Different architectures of Neural Networks and their advancement over time.
• Introduction to Deep Learning and PyTorch framework for modelling.
• Introduction to Recurrent Neural Network and its applications in financial domain.
• Introduction to Auto-Encoders and its applications in financial domain.
• Introduction to Large Language Models and its applications in financial domain.
• Hands-on workshops and project.
• Evaluation of ML/AI applications in financial domain.
• High-level introduction to MLOps and Responsible AI.

Fees & Subsidies

1. All fees and subsidies are valid from January 2024, unless otherwise advised.
2. 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.
3. From 1st January 2024, the GST will be increased to 9%.

Certificate

Certificate of Completion
The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course.

Assessment Plan
This course aims to assess the participants’ technical Skills & competencies through the following means.

Assessors will be domain experts from the ISS Teaching Staff (Instructor for this course).

To be deemed ‘Competent’ for each learning objective (skills & knowledge), participants must have achieved a mark of ‘Competent’ in all of the corresponding assessed activities.

TSC Description: Develop, select and apply algorithms and advanced computational methods to enable systems or software agents to learn, improve, adapt and produce desired outcomes or tasks. This also involves the interpretation of data, including the application of data modelling techniques to explore and address a specific issues or requirements.

TSC Proficiency Description: Level 5 

• Develop and utilise new algorithms and advanced statistical models to enable production of desired outcomes
• Knowledge
  • Industry developments and trends in analytics, algorithms and statistical modelling
  • New and emerging data analytics and modelling tools and methodologies
  • Broad range of algorithms and advanced programming techniques
  • Elements of complex or advanced algorithms and computational models
  • Applicability of various data analytics methodologies and techniques to address different business issues
• Abilities
  • Direct data analytics and statistical modeling efforts across the organisation
  • Make decisions on appropriate data analytics and computational methodologies to the problem
  • Design complex or advanced statistical and computational methods
  • Evaluate a brad range of algorithms and advanced computational methods to determine suitability for business context
  • Spearhead the application of algorithms, models and computational techniques to new domains
  • Establish guidelines for the creation and selection of effective algorithms and statistical models
  • Synthesis critical findings and insights to address a significant business need or problem

Join Us

Preparing for Your Course

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

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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.