Most AI Deep Learning and data science projects fail, despite promising test results and clear problem statements at the start. This is because many users may start off with unrealistic expectations due to an inadequate understanding of AI; what it can and cannot do, and IT managers may fail to understand the privacy, ethics, cyber security and governance issues related to this new disruptive science. Additionally, there may not be enough high quality data available, and users do not understand the importance of garbage in, garbage out.
Therefore, the aim of this module is to provide a business deployment perspective of foundational Deep Learning AI capabilities, ranging from Natural Language Processing to Machine Vision; in a manner that requires little or no coding. Neural networks and how Deep Learning works, the data input requirements, project lifecycle and the development framework would be introduced. What problems that each specific AI algorithm can solve would be illustrated in an interactive workshop session and the enterprise benefits would be highlighted via case studies; e.g. enhanced productivity for corporate decision making, process automation or difficult unattended problem solving.The focus is on rapid prototyping and to guide the users and the relevant project team members to validate project viability using the data and requirements that are available. Importantly, the student will gain the skills to highlight missing gaps, failure scenarios and to discuss AI compliance, privacy and cyber security requirements at the design phase, avoiding misunderstanding of the project’s complexity and hence be able to protect the project from failure.
Finally, modern challenges such as ensuring man-in-the-loop AI oversight, cyber security hardening against adversarial AI attacks and privacy obligations would be covered as new requirements for an upgraded corporate IT risk management framework that will be instrumental in protecting the company’s digitalisation efforts.
The future of AI continues to shine brightly, and we aim to highlight major high value factory automation future possibilities as AI and Big Data systems converge pervasively, and 5G and IoT AI at the edge become commodity. With these insights, the student will master key leadership skills to manage and plan AI projects successfully and hence, deliver strategic benefits to the corporation.
A Certificate of Attainment will be issued to participants who:
| Module | Date and Timing (9am - 1pm) |
| 1 | 6 Sep 2021 (Physical Class) |
| 2 - 5 | Online (Asynchronous e-learning) |
| 6 | Physical/ Zoom Class |
| 7 - 12 | Online (Asynchronous e-learning) |
| Exam | 22 Oct 2021 |
| Category | Full Fee | After SF Funding |
| Singapore Citizen (Below 40) / Singapore PR |
$1,733.40 | |
| Singapore Citizen (40 & above) | $653.40 | |
| Non-Singaporeans | $5,778.00 | Not Eligible |
Find out more about the Modular Certification Courses - Data Engineering and Smart Factory.
These modules are stackable towards a Postgraduate Certificate in Data Engineering and Smart Factory (PGCert in DESF) or can be taken individually as a single module as follows:
| Venue | SIT@Dover, 10 Dover Drive S138683/ Online |
| Time | 09:00 AM to 01:00 PM |
| Date |
06 Sep 2021 (Mon) to 22 Oct 2021 (Fri) |
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