The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Lead and revolutionise your business strategies with Artificial Intelligence applications.
TBD
2 months, online
4-6 hours per week
Our participants tell us that taking this programme together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for AI-led Business Strategies starting on TBD .
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The innovations of digital technology continue to transform industries. Of these innovations, artificial intelligence (AI) has made a significant impact on the way businesses operate. In the AI-led Business Strategies programme, offered by Singapore Management University, you will explore how to apply the latest AI methodologies to lead digital transformation within your organisation while upscaling your business skillset—no coding experience is required. The programme is designed to expand your understanding of AI and teaches the methodologies used for applying it to business scenarios and strategies and build your leadership credibility.
The programme is designed for professionals who aspire to learn more and expand their knowledge of AI and its applications in business scenarios and strategies. 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:
Looking to:
113 Video Lectures
40+ Industry Examples
19 Assignments
8 Discussion Boards
4 Case Studies
Associate Professor of Marketing
Sandeep serves as Associate Professor of Marketing. Prior to joining SMU, Prof Sandeep worked at Indiana University's Kelley School of Business and was a 3M Jr faculty fellow at Indiana University. 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
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
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
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
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 infoSandeep R. Chandukala, PhD
Associate Professor of Marketing
Sandeep serves as Associate Professor of Marketing. Prior to joining SMU, Prof Sandeep worked at Indiana University's Kelley School of Business and was a 3M Jr faculty fellow at Indiana University. He has a PhD in Marketing (with a minor in Statistics) from The Ohio State University, MS (MAS) and MBA from University of Texas at Dallas, and MS (Computer Engineering) from University of Minnesota.
Associate Professor Chandukala’s research interests are related to developing quantitative models of consumer behaviour using industrial data. His research primarily focuses on retail analytics. Specifically, understanding and measuring the impact of promotions, advertising and new products and proposing new approaches for market segmentation using Bayesian and Markov Chain Monte Carlo (MCMC) methods. His research has appeared in Marketing Science, Journal of Marketing, Journal of Retailing, Marketing Letters and Customer Needs and Solutions. Associate Professor Chandukala was awarded Lee Kong Chian Research Fellowship in 2016-17 and was also in the Dean’s Teaching Honour List for Postgraduate Teaching in 2018.
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. AI research group, with interests centred on Web mining, as well as preference analytics and recommendations. He has been awarded several prestigious fellowships such as National Research Foundation Fellowship, Lee Kong Chian Fellowship, and Educational Research Fellowship.
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 for the MITB Artificial Intelligence programme. Prior to his time at SMU, Assistant Professor Dai Bing Tian was a Research Assistant for the School of Computing at the National University of Singapore, where he earned his PhD.
Assistant Professor Dai Bing Tian has taught SMU courses related to his research interests and expertise, including Computer as an Analysis Tool, Modeling and Analytics, Algorithm Design and Implementation, Machine Learning and Data Science and Engineering. His work has appeared in scientific journals—including IEEE Transactions on Pattern Analysis and Machine Intelligence and Information Systems—as well as over 20 conference proceedings.
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 as a judicial officer, corporate lawyer and in-house legal counsel. He has obtained postgraduate degrees in Law, Philosophy and Southeast Asian studies, and his main research interests are Tort Law, the Singapore Legal System, Health Law and Ethics including ethics in relation to artificial intelligence and medicine.
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 Data Protection Law Information Technology and the Law Intellectual Property Law. Currently, Associate Professor of Law Warren Chik is researching and writing treatises and journal articles on Information Technology Law and its intersection with copyright law and data protection regulations in particular.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Through real-world examples from multiple industries, you'll see how the theories are put into practice.
Learn about how Netflix held a competition for anyone who could improve their algorithm's performance by more than 10%.
Analyse how Tik Tok leveraged consumer focused AI to grow globally at a rapid pace.
Understand how Uber uses AI for auto-fill text feature using text mining.
Learn how google uses AI to provide real time updates while looking at live traffic feeds.
Analyse how Spotify provides recommendations through AI driven recommendation systems.
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.
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.
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.
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.
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.
Upon successful completion of the programme, participants will be awarded a verified digital certificate by Singapore Management University.
Download BrochureAll certificate images are for illustrative purposes only and may be subject to change at the discretion of Singapore Management University.