Artificial Intelligence for Managers

Part of the ModularMaster in Technology and Management programme

Artificial Intelligence (AI) brings rapid changes to all sectors of society and opens opportunities and needs for new solutions. To succeed in the competition, organisations and individuals need new technological skills as well as a clear understanding of the big picture of AI. What is AI, what are the current AI technologies and how can they be developed and deployed?

Artificial Intelligence for Managers gives you in-depth understanding of the topic and helps you to understand and apply contemporary AI technologies. The lessons are complemented by exercises and possible project work, which enable you to apply the skills acquired right away in your daily work.

Who Should Attend

The course is for business and technology developers who need both practical skills and in-depth understanding to utilise artificial intelligence technologies.

We recommend the course for Programmers and Developers, Product Managers, Business Development Managers and Directors, Deployment Managers, Software Architects, IT Managers and Directors.

Programme Outline

Learning Objectives

By the end of this course, participants can expect to:

Understand differences between different AI / ML methods
Know how to select the right method for a given problem
To be able critically evaluate the results of different methods
To be able to translate a business problem into a Machine Learning problem

Day 1

Introduction to the module and faculty, programme flow and pre-assignments.
Project instructions, project briefing and assignment of groups
Assignment of prework
Learning of key concepts from pre-reading material set by professors
Preparation of cases set by professors
Identification of personal learning goals of the current module set by oneself

Day 2

Learn the Three Components of Machine Learning: Data, Model and Loss
Understand AI through groupwork by modelling real-life situation
Deep dive into Machine Learning: Deep Learning, Model Validation and Selection, unsupervised machine learning
Learn to use clustering methods for grouping large collections of data points into few coherent clusters
Learn to use dimensionality reduction methods to learn relevant features of a data point
Acquire hands-on experience in applying basic methods in AI through groupwork
Explore opportunities and challenges of applying AI in business

Day 3

Overview and history of AI
Understanding the relationship between data and AI
Discussion of case studies on the use of AI in business
Algorithms for recommending items for consumers
Approaches for detecting anomalies in data
Hands-on exercise

Day 4

Understanding and processing text in business
Tools for reinforcement learning
Hands-on exercise
Methods for explaining outputs from AI systems
Understanding the limitations of AI
AI Ethical and Societal Issues
Wrap up and next step

Day 5

Project presentation

Assessment

Multiple methods of assessment are used to provide an opportunity for the participant to demonstrate their learning results with a variety of learning styles.

These include:

Pre-assignment due before in-class session
Class contribution
In-class assignments (group work and presentations during the module)
Take-home assignment due after in-class session

Subject Credits

Upon completion and satisfying the requirements of passing this course, learners will be awarded 12 subject credits.

Course Fees and Funding

Full course fee inclusive of prevailing GST	You pay S$6,758.00
SkillsFuture Course Fee subsidy (70%) For Singapore Citizens < 40 years old For Permanent Residents	You pay S$2,027.40
Mid-Career Enhanced Subsidy (90%) For Singapore Citizens ≥ 40 years old	You pay S$787.40
Enhanced Training Support for SMEs (90%) For SME - Sponsored employees	You pay S$787.40

The above module fee payable is inclusive of 9% GST.

Instructors

Assistant Professor Kwan Hui Lim
Fellow, SUTD Academy
Assistant Professor at SUTD

Kwan Hui LIM is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design, and leads the Social and Urban Analytics Lab. Previously, he was a Research Fellow at the School of Computing and Information Systems, University of Melbourne, Research Engineer at the Living Analytics Research Centre, Singapore Management University, Research Intern at IBM Research – Australia, and various visiting appointments. He received his PhD from the University of Melbourne, and MSc (Research) and BCompSci (1st Class Honours) from the University of Western Australia.

He is a recipient of the 2016 Google PhD Fellowship in Machine Learning. His research interests are in Data Mining, Machine Learning, Artificial Intelligence, Social Network Analysis, and Social Computing.

Assistant Professor Alexander Jung
Assistant Professor for Machine Learning at Aalto University

Alexander Jung is an Assistant Professor for Machine Learning at Aalto University in Finland. Prior to joining Aalto, he obtained a PhD in statistical signal processing from TU Vienna in 2012 and spend Post-Doc periods at TU Vienna and ETH Zurich. Alex leads the Aalto research group “Machine Learning for Big Data” which studies the fundamental limits and efficient algorithms for machine learning from large distributed collections of data (big data over networks).

His current research focus is on privacy preserving and explainable federated machine learning methods for big data over networks. Alex has developed some of the most popular courses at Aalto University. His course "Machine Learning with Python" is the most popular course across the entire university network fitech.io.

He has been selected as the Teacher of the Year by the Department of Computer Science in 2018. The lecture notes developed during his teaching are currently turned in a textbook “Machine Learning. The Basics” to be published in 2022.

Policies and Financing Options

SSG Funding Terms and Conditions

Use of Personal Details

In consideration of the subsidy provided by SkillsFuture Singapore Agency (“SSG”) through the SUTD Academy for the Course,

I consent to:

The collection, use and disclosure to relevant third parties of my personal data by the SUTD Academy including but not limited to personal particulars, attendance records, assessment/performance records, for the following purposes:

Reporting of national statistics and conducting of holistic continuing education training research and analysis;
Facilitate the conduct of the relevant surveys and audits in relation to the Course;
General administration of the Course including but not limited to processing of the subsidy provided by SSG;
Publicity and marketing of the Course or other Courses to be provided by SSG or SUTD Academy; and
SSG or its Appointed Auditors or Nominated Representatives to directly contact Course Participant to obtain information deemed necessary for the purposes of conducting effectiveness survey or audits in relation to the Course.

I agree to:

Attend and complete all lectures, class exercises, workshops and assessments;
Complete the Course feedback at the end of the Course;
Complete the post Course survey sent about 3 to 6 months after class attendance; and
Sign up for a personal email account.

SUTD Privacy Statement

For more information on SUTD's privacy statement, please visit https://sutd.edu.sg/Privacy-Statement.

SUTD Terms and Conditions

Methods of Payment

Learn more about the available payment modes.

Cancellation & Refund Policy

If a written notification is sent to sutd_academy@sutd.edu.sg within 24 hours after course registration deadline there will be no cancellation charges. A full refund will be made.
No refund is provided if written notification is more than 24 hours after course registration deadline. SUTD Academy reserves the rights to collect the full fee amount from the participant.

Replacement Policy

Companies may replace participants who have signed up for the course by giving a 3-working day notice before the course commencement date to sutd_academy@sutd.edu.sg. Terms and conditions apply.

Registration Policy

Course may be cancelled due to insufficient participants. SUTD Academy will not be responsible or liable in any way for any claims, damages, losses, expenses, costs or liabilities whatsoever (including, without limitation, any direct or indirect damages for loss of profits, business interruption or loss of information) resulting or arising directly or indirectly from any course cancellation.
Course enrolment is based on a first-come, first-served basis.
SUTD Academy reserves the right to change or cancel any course or instructor due to unforeseen circumstances.

Types of Funding

Funding under Mid-Career Enhanced Subsidy ("MCES")

MCES is an enhanced Subsidy to encourage mid-career individuals to upskill and reskill, thereby helping them to remain competitive and resilient in the job market. With this, all Singaporeans aged 40 and above will receive higher subsidies of up to 90% course fee subsidy for SSG-funded certifiable courses.
Individuals/employers are not required to submit an application for the MCES. Those pursuing SSG-funded programmes will be charged the appropriate subsidised fees by SUTD Academy if they are eligible MCES. Individuals/employers will only need to pay the nett fee (full course fee after SSG's grant).

For more info, please visit SkillsFuture website at https://www.skillsfuture.gov.sg/enhancedsubsidy

Funding under Enhanced Training Support for SMEs ("ETSS")

ETSS is an enhanced funding to enable SMEs to send their employees for training.
SMEs will enjoy subsidies of up to 90% of the course fees when they sponsor their employees for SSG-funded certifiable courses.
In addition to higher course fee funding, SMEs can also claim absentee payroll funding of 80% of basic hourly salary at a higher cap of $7.50 per hour. SMEs may apply for the absentee payroll via the SkillsConnect system.
To qualify, SMEs must meet all of the following criteria:
- 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

For more info, please visit SSG website at https://www.ssg.gov.sg/programmes-and-initiatives/funding/enhanced-training-support-for-smes1.html

Funding under Union Training Assistance Programme ("UTAP")

UTAP is a training benefit for NTUC members to defray their cost of training. This benefit is to encourage more union members to go for skills upgrading.

NTUC members enjoy 50% unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP (Union Training Assistance Programme).

For more info, please visit https://e2i.com.sg/individuals/ntuc-education-and-training-fund/.

Funding under Post-Secondary Education Account ("PSEA")

The Post-Secondary Education Account (PSEA) is part of the Post-Secondary Education Scheme to help pay for the post-secondary education of Singaporeans.

This is part of the Government’s efforts to encourage every Singaporean to complete their post-secondary education. It also underscores the Government’s commitment to support families in investing in the future education of their children and to prepare them for the economy of the future. PSEA is not a bank account.

It is administered by the Ministry of Education (MOE) and is opened automatically for all eligible Singaporeans.

Account holders can use their PSEA funds to pay for their own or their siblings’ approved fees and charges for approved programs conducted by approved institutions.

However, you will have to check your eligibility and balance by contacting MOE first.

Contact MOE at (65) 6260 0777

E-mail to MOE at contact@moe.edu.sg

Click here for MOE website.