AI and ML: Strategies and Frameworks for Decision-Making

Explore artificial intelligence and machine learning applications revolutionising businesses.

Get Your Brochure

Course Dates

STARTS ON

15 December 2021

Course Duration

DURATION

2 months, online
4-6 hours per week

Course Duration

PROGRAMME FEE

US$1,400

Course Information Flexible payment available
Course Information Special group enrolment pricing

Enjoy a special holiday offer by applying today! Use code SMU150EBTA during payment to receive US$ 150 off the programme fee.

Have a chat with us - +65 3129 5093 (Monday-Friday) (11:30 AM - 06:30 PM Singapore time)

Why Enrol in AI and ML: Strategies and Frameworks for Decision-Making?

The innovations of digital technology continue to transform entire industries. Of these innovations, artificial intelligence (AI) and machine learning (ML) have made a significant impact on the way businesses operate. From start-ups to medium-sized companies to large enterprises, AI and ML are being used to improve efficiency and productivity; expand business capabilities and opportunities; and develop data-backed, innovative solutions.

66%

of c-suite executives note that their organisations are either already using AI and machine learning technology or are planning to invest in them.

SOURCE: Digital Transformation Trends Report, 2020

50%

of survey respondents report that their companies have adopted AI in at least one business function.

SOURCE: MCKINSEY GLOBAL SURVEY, 2020

USD 309 Billion

The global AI/ML market size is expected to grow by USD 251 Bn from 2021–2026, surpassing USD 309 Bn. APAC is expected to experience the fastest-growing CAGR during this forecast period.

SOURCE: MARKETSANDMARKETS, 2020

Programme Faculty

Faculty Member Sandeep R. Chandukala, PhD

Sandeep R. Chandukala, PhD

Associate Professor of Marketing

Sandeep serves as Associate Professor of Marketing. Prior to joining SMU, Sandeep worked at 3M and before that he worked as a Jr Faculty Fellow at the Indiana University’s Kelley School of Business. He has a PhD in Marketing (with a minor in Statistics) from The Ohio State University, MS (MAS) and MBA from University of Texas... More info
Faculty Member Hady W. Lauw, PhD

Hady W. Lauw, PhD

Associate Professor of Computer Science; Director, BSc (Computer Science) Programme; Lee Kong Chian Fellow

Dr Hady W. Lauw is an Associate Professor of Computer Science in the School of Computing Information Systems, where he teaches Data Structures and Algorithms, Recommender Systems, and Machine Learning. He leads the Preferred... More info
Faculty Member Dai Bing Tian, PhD

Dai Bing Tian, PhD

Assistant Professor of Computer Science Education; Director, MITB Artificial Intelligence Programme

Assistant Professor Dai Bing Tian has held various academic appointments at SMU since 2010, including as a Research Engineer, Fellow and Scientist. Currently, he serves as the Assistant Professor of Computer Science Education as well as the Director... More info
Faculty Member Gary Chan Kok Yew

Gary Chan Kok Yew

Professor of Law

Professor Gary Chan has been a faculty member at Singapore Management University for close to two decades, teaching courses on Ethics and Social Responsibility, Law of Torts, Legal System, Methods and Analysis, Health Law and Medical Ethics, amongst others. Prior to joining academia in 2002, he had served... More info
Faculty Member Warren B. Chik

Warren B. Chik

Associate Professor of Law; Deputy Director, Centre for AI and Data Governance

Associate Professor of Law Warren Chik is the Deputy Director of the Centre for AI and Data Governance. He teaches courses on Business Law, Entertainment Law, Information Technology and the Law at SMU. Other teaching and research interests include... More info

Programme Highlights

113 Video Lectures

113 Video Lectures

40+ Industry Examples

40+ Industry Examples

19 Assignments

19 Assignments

8 Discussion Boards

8 Discussion Boards

4 Case Studies

4 Case Studies

Programme Modules

The programme comprises 8 modules. Each module is led by an SMU faculty expert with field experience specific to the AI/ML topics being discussed.

Module 1:

An Overview of AI and ML

Explore the impact of different data types and analytics for business, how to determine if your organisation is data-driven, and the best practices for AI and ML: Strategies and Frameworks for Decision-Making.

Module 2:

AI and ML Technologies Survey

Understand differences between regression and classification, facets of machine learning and neural networks, supervised and unsupervised learning principles, impact of transformer-based architecture on language understanding, and representation learning models.

Module 3:

AI and ML Applications and Trends – Part 1

Gain a deeper understanding of machine learning techniques and workflow; how to analyse neural networks and deep learning; and effective evaluation of AI applications in retail, healthcare, manufacturing and service sectors.

Module 4:

AI and ML Applications and Trends – Part 2

Continue to examine how AI is applied across sectors, including in finance, energy, education and security sectors; the benefits and limitations of AI; and how AI can be leveraged as a driver for AI and ML: Strategies and Frameworks for Decision-Making.

Module 5:

AI-Led Business Transformation and Identifying Opportunities

Explore AI strategies and potential reasons for failure, build vs. buy platforms, various facets of the future of AI, and how to identify and align AI opportunities with business strategies.

Module 6:

Framework for Development and Deployment of AI and ML Technologies

Build your knowledge of the machine learning life cycle; feature engineering and differentiation between nominal, ordinal and text data; analysing model evaluation with regard to training, validation and metrics; model deployment and database design; and how random forest can improve decision trees by reducing overfitting.

Module 7:

Ethics

Examine the concept of ethics and its significance for organisations; the purpose and relevance of ethical codes and guidelines with regards AI and ML: Strategies and Frameworks for Decision-Making; addressing potential challenges involved in ethics and industry applications; and the values and ideas related to AI and ML: Strategies and Frameworks for Decision-Making, technology, data, and ethics.

Module 8:

Privacy and Regulations in AI and ML

Dive into guidelines for ethics and trust related to AI, regulatory governance in the development and deployment of AI, AI-specific regulations in law, personal data protection and the impact of AI on data rights and obligations, the role of AI in eCommerce and the legality of electronic advertising and marketing, and how to devise an organisational plan to improve AI compliance.

Module 1:

An Overview of AI and ML

Explore the impact of different data types and analytics for business, how to determine if your organisation is data-driven, and the best practices for AI and ML: Strategies and Frameworks for Decision-Making.

Module 5:

AI-Led Business Transformation and Identifying Opportunities

Explore AI strategies and potential reasons for failure, build vs. buy platforms, various facets of the future of AI, and how to identify and align AI opportunities with business strategies.

Module 2:

AI and ML Technologies Survey

Understand differences between regression and classification, facets of machine learning and neural networks, supervised and unsupervised learning principles, impact of transformer-based architecture on language understanding, and representation learning models.

Module 6:

Framework for Development and Deployment of AI and ML Technologies

Build your knowledge of the machine learning life cycle; feature engineering and differentiation between nominal, ordinal and text data; analysing model evaluation with regard to training, validation and metrics; model deployment and database design; and how random forest can improve decision trees by reducing overfitting.

Module 3:

AI and ML Applications and Trends – Part 1

Gain a deeper understanding of machine learning techniques and workflow; how to analyse neural networks and deep learning; and effective evaluation of AI applications in retail, healthcare, manufacturing and service sectors.

Module 7:

Ethics

Examine the concept of ethics and its significance for organisations; the purpose and relevance of ethical codes and guidelines with regards AI and ML: Strategies and Frameworks for Decision-Making; addressing potential challenges involved in ethics and industry applications; and the values and ideas related to AI and ML: Strategies and Frameworks for Decision-Making, technology, data, and ethics.

Module 4:

AI and ML Applications and Trends – Part 2

Continue to examine how AI is applied across sectors, including in finance, energy, education and security sectors; the benefits and limitations of AI; and how AI can be leveraged as a driver for AI and ML: Strategies and Frameworks for Decision-Making.

Module 8:

Privacy and Regulations in AI and ML

Dive into guidelines for ethics and trust related to AI, regulatory governance in the development and deployment of AI, AI-specific regulations in law, personal data protection and the impact of AI on data rights and obligations, the role of AI in eCommerce and the legality of electronic advertising and marketing, and how to devise an organisational plan to improve AI compliance.

Download Brochure

Industry Examples

Through real-world examples from multiple industries, you'll see how the theories are put into practice.

Netflix

Netflix

Learn about how Netflix held a competition for anyone who could improve their algorithm's performance by more than 10%.

Tik Tok

Tik Tok

Analyse how Tik Tok leveraged consumer focused AI to grow globally at a rapid pace.

Uber

Uber

Understand how Uber uses AI for auto-fill text feature using text mining.

Google maps

Google maps

Learn how google uses AI to provide real time updates while looking at live traffic feeds.

Spotify

Spotify

Analyse how Spotify provides recommendations through AI driven recommendation systems.

Alexa

Alexa

Examine how voice assistance is possible through AI.

Other companies that are included as examples or as use cases in the programme include Amazon, Microsoft Tay's chatbot, Q, Starhub, DBS, Coca Cola, American Express, Tesla, Sobeys, Argus etc.

Note: All brand, product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with, endorsement by, or association of any kind between them and programme owners.

Case Studies

Analytics-Driven Transformation at Majid Al Futtaim

Analytics-Driven Transformation at Majid Al Futtaim

Explore how a lifestyle conglomerate with annual revenues over USD 9 Bn designed and employed a data-led, customer-centred transformation strategy to unlock new sources of growth and mitigate the effects of the Covid-19 pandemic on business.

A Deep Learning Start-Up Powers the Internet of Things

A Deep Learning Start-Up Powers the Internet of Things

Explore how Preferred Networks, Inc—a start-up specialising in deep learning technologies—differentiated itself early on by aligning with Japan's manufacturing might and bringing deep learning to the Internet of Things.

Vispera: Visual Intelligence for Retail

Vispera: Visual Intelligence for Retail

See how computer-vision tech provider Vispera used technology and its automated visual analysis to help retail and fast-moving consumer goods companies around the world minimise stockouts, increase sales, reduce personnel costs and improve operational efficiencies.

TikTok’s AI Strategy: ByteDance’s Global Ambitions

TikTok’s AI Strategy: ByteDance’s Global Ambitions

Explore how ByteDance—the company behind TikTok—leveraged capabilities in consumer-focused AI to become one of the first Chinese Big Tech digital platforms to succeed outside China, notably in the US and India.

Who Is This Programme For?

The programme is designed for professionals who aspire to learn more about AI and ML and its applications across different sectors. Representative industries and roles that can benefit include:

Industries:
Banking and Financial Services, Healthcare, Education, IT Products and Services, Consulting, E-Commerce, Telecommunications, Retail, FMCG

Functions:
Technological Management, General Management, Operations, Finance, IT, Consulting

Responsible for:

  • Developing speedy, innovative solutions via technological practices
  • Interpreting and managing data
  • Leading an organisation, business vertical and/or entire function
  • Expanding business/launching new divisions
  • Leveraging business deals using AI and ML

Looking to:

  • Discover industry-relevant AI/ML techniques
  • Invest in AI/ML projects that can move product/service research faster and with greater predictability
  • Target new market segments and work on new business lines
  • Improve organisational rollout of products/services

What Will This Programme Do For You?

  • Survey AI technologies and their uses across sectors and functions
  • Identify opportunities within a business to implement AI and ML for better performance
  • Recognise the purpose of ethics and challenges in industry applications
  • Explain the steps to build and deploy AI solutions for business/sector
  • Examine the AI-specific privacy regulations in law
  • Create an executive summary of an AI implementation/improvement plan for a business

Certificate

Certificate

Upon successful completion of the programme, participants will be awarded a verified digital certificate by Singapore Management University.

Download Brochure

All certificate images are for illustrative purposes only and may be subject to change at the discretion of Singapore Management University.

Apply Now

Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.