Introduction
The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. At the same time, developments in AI are yielding benefits for neuroscientific research. Patterns identified from neural networks can illuminate computations enacted by the biological brain, functioning both as a model for developing and testing ideas about how the brain performs computations. Conversely, brain-activity recordings can be fed to an artificial neural network and tasked with learning how to reproduce the data, functioning as a tool for processing complex data sets that the Science of Learning research field is generating. This course will explore cycles of mutual reinforcement between neuroscientific data and artificial neural networks to obtain further insights into how computation works in the brain, and how machines that can take on more human-like intelligence to advance understanding for how a learner develops. Specifically, the course will focus on unexplored spaces at the intersections of neural AI, symbolic AI, brain science and cognitive science. Takeaways include implications for education and how cutting edge teaching and learning methodologies harnessed from AI and SoL fields may be developed.
This course is part of:
- Graduate Certificate in Mind, Brain, and Education
- FlexiMasters in Mind, Brain, and Education
Learners need to complete any of the following three sets of courses to attain the Graduate Certificate in Mind, Brain, and Education (12 AU):
- MSL901A, MSL901B, and MSL901C OR MSL901
- MSL904A, MSL904B, and MSL904C OR MSL904
- MSL906A, MSL906B, and MSL906C OR MSL906
- MSL907A, MSL907B, and MSL907C OR MSL907
Upon completion of the fourth set of courses, learners will fulfil the AU requirements to attain the FlexiMasters in Mind, Brain, and Education (16 AU).
Download Learning Pathway E-Guide
Topics covered:
- Part 1: Introduction to Computational Neuroscience using the AI
programming tool Matlab
- Part 2: Analyses of Time Series Data from EEG using the AI programming
tool Matlab
- Part 3: Analyses of Action Potentials in Neurons using the AI programming
tool Matlab
- Part 4: Analyses of Magnetic Resonance Images using the AI programming
tool Matlab
For experienced professionals in:
a. Early childhood, K12, Tertiary, and Adult education
b. Healthcare education
c. Professional and staff development
d. Quality assurance and regulation of educational
institutions, e.g., Council for Private Education (CPE)
e. Continuing education and training (CET)
Standard Course Fee: S$4,926.80
COURSE TITLE | ACADEMIC UNIT |
MSL901A Developments in Science of Learning | 1 |
MSL901B Cognitive and Affective Science of Learning | 1 |
MSL901C The ‘science’ of Human Learning | 2 |
MSL904A Theories of Educational Neuroscience | 1 |
MSL904B Perspectives in Educational Neuroscience | 1 |
MSL904C Social Neuroscience | 2 |
MSL906A Foundations of AI-enabled Computational Neuroscience | 1 |
MSL906B Analyses using AI-enabled Computational Neuroscience | 1 |
MSL906C Technologies at the intersections of Artificial Intelligence and Neuroscience | 2 |
MSL907A Translational Research: An overview | 1 |
MSL907B Ethical concerns of translating Educational Neuroscience | 1 |
MSL907C Science of Learning: From Theory to Practice | 2 |
MSL901 Foundations in Science of Learning | 4 |
MSL904 Educational Neuroscience: Principles, Perspectives, Practices | 4 |
MSL907 Translating Educational Neuroscience | 4 |
Listed courses are:
- Credit-bearing and stackable to Graduate Certificate in Mind, Brain, and Education (total 12AUs) and FlexiMasters in Mind, Brain, and Education (total 16AUs)
*SSG funded and SkillsFuture Credit approved