Key Takeaways
At the end of the course, the participants will be able to:
- Gain insight into the functions and uses of AI-driven Recommender Systems.
- Recognise the essential data types required for constructing a recommender system.
- Comprehend the primary categories of AI-based recommender systems and determine the appropriate circumstances for each.
- Construct AI-based Recommender Systems employing statistical modeling, Artificial Intelligence (AI), and Machine Learning (ML) algorithms.
- Evaluate the effectiveness and precision of AI-based Recommender Systems via a comprehensive testing and validation lifecycle.
Who Should Attend
This course is intended for data analysts, marketers or anyone who is interested in leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to use data and analytics to understand customer behaviours and preferences and to make personalised AI-based recommendations to users.
Recommender Systems can be applied in various other domains such as
- Healthcare: Recommending personalised treatment plans medications, and lifestyle changes based on patient history and preferences;
- Finance: Offering investment advice, personalised financial planning, and targeted promotions for banking products based on user profiles and transaction histories;
- Human Resources: Assisting in recruitment by recommending suitable job candidates and career development paths for employees.
- Tourism and Hospitality: Recommending travel destinations, hotels, activities, and personalised itineraries based on user preferences and past travel behavior.
- Retail and Marketing: Retail companies use recommender systems to offer personalized promotions and discounts to customers based on their shopping behaviours and preferences
Prerequisites
This is an intensive and advanced course. It contains workshops that are conducted using python and basic knowledge of the python language is required.
You will be required to pass a pre-course assessment to ensure that you have the requisite background knowledge to learn the material. This assessment is based on material covered in the
Statistics Bootcamp II course. This assessment can be waived if you have completed the Statistics Bootcamp II course.
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
|
|
|
Others
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• 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
|
What Will Be Covered
This course will cover:
- Introduction to AI-based Recommender Systems, exploring foundational concepts and methodologies underpinning AI and ML in recommendation systems.
- Making AI-based recommendations using Market Basket Analysis methods to analyse transaction data and generate relevant recommendations.
- Making AI-based recommendations using Content-Based approaches to recommend items based on their attributes and user preferences.
- Making AI-based recommendations using Collaborative Filtering (part A)
- Making AI-based recommendations using Collaborative Filtering (part B)
- Advanced AI-based Recommender Systems Approaches & Issues
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 have to meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.
Join Us
Master Recommender Systems.
Register now and leverage AI for smarter decision-making.
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.