Executive Education Programmes designed to build capabilities in infocomm and digital business.
Course Planner 2018
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We offer five practice-based graduate programmes focusing on information technology (IT) and data science.
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Clusters of expertise focusing on building leadership, best practice, and capability development in areas of Digital Government and Smart Health.
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This Masterclass will give participants an update on the latest advances in Deep Learning from the industry perspective and more significantly provides a practical jumpstart into Deep Learning using Deep Neural Networks in computer vision applications.
Please contact Ms. Maybelline NEO Manlin at tel: 65167646 or email to firstname.lastname@example.org for more details.
Conducted by: David and Jawad
Introducing Computer Vision, different applications of CV, approach with hand-crafted feature extractors.
Inspired by biological neurons, brief on neural network and define deep neural network. The first paper that toys around the idea and the experiment will be mentioned.
Overview on multi-layer perceptron and origins of convolutional neural network. Concepts such as Local Connectivity, Spatial arrangement, constraints on strides and use of zero padding are introduced.
Sharing network architectures of CNN, namely LeNet, AlexNet, ResNet, etc. Building a ConvNet, e.g.: Input, Conv, ReLU, Pool, Fully-connected layers.
Illustrate the accuracy of network architectures on Imagenet dataset.
Making deep learning more transparent by visualizing the convolutional units. Sneak peek into a neuron to better understand workings of neural networks.
Latest architecture, applications and how well it fare against previous approaches.
Sharing tips on model training and general rule of thumb on setting various parameters.
Big tech companies, e.g. Google, Facebook, Amazon, etc have their own deep learning framework. Learn the pros and cons as a starter.
Conducted by: Weimin and Zane
Code walkthrough and how TensorFlow API works. Participants are expected to apply what they learn in the walkthrough session and produce an accurate model on the other dataset provided.
Programme may be subjected to changes.
David Low is the Co-founder and Chief Data Scientist at Pand.ai, building AI-powered chatbot to disrupt and shape the booming conversational commerce space with Deep Natural Language Processing. Pand.ai is the only AI chatbot startup accepted into Nvidia Inception Program in the region and currently serves two Fortune Global 500 companies in the financial sector. David represented Singapore and National University of Singapore (NUS) in the Data Science Game'16 held in France and clinched top spot among Asian and American teams.
Throughout his career, he has engaged in data science projects ranging from Manufacturing, Telco, E-commerce to Insurance industry. Some of his works, including sales forecast modeling and influencer detection, had won him awards in several competitions and were featured on IDA's website and NUS publication. Earlier in his career, David was involved in research collaborations with Carnegie Mellon University (CMU) and Massachusetts Institute of Technology (MIT) on separate projects funded by National Research Foundation and SMART. As a pastime activity, he competed on Kaggle and achieved Top 0.2% worldwide ranking. He is occasionally invited to speak at local and overseas data science conference/events such as Analytics Leaders Summit, Taiwan Fintech Convention, DBS Machine Learning workshop and Deep Learning Summit 2017.
Zane is a data science and machine learning practitioner and an AI enthusiast. He is currently working as a senior data scientist, leading a sub-team of data scientists at Go-Jek, Indonesia's first and largest unicorn technology firm. His job involves end-to-end predictive modelling application, from building data pipelines to machine learning modelling to engineering real-time deployment.
Off-work, Zane has participated in various data science hackathons and won good standings in a few of them, with the highlight being one of the Singaporean representatives at the worldwide Data Science Games competition held in Paris. He is an avid Kaggler and a mentor of Udacity's Artificial Intelligence nanodegree programme.
Jawad is a Data Scientist with 4 years experience in Industry and Research. Some of his work includes simulation modelling for NUS School of Design and Environment, and anomaly detection for financial institutions. construction workers. His current role at Go-Jek involves solving high impact business problems through data science, machine learning and plain old software engineering.
Jawad believes in lifelong learning and spends his spare time participating in Hackathons/Kaggle and keeping up to date with emerging technologies such as deep learning.
Weimin has 5 years of experience in Data Science and Machine Learning and holds a Master's degree in Statistics from NUS. Throughout his career, Weimin has accomplished various research and industrial projects, including Drug Discoveries using Machine Learning, which is a collaborative work between Merck and Stanford. He has also published papers, as well as represented Singapore and NUS in the 2016 Data Science Game held in Paris, where his team clinched top rankings globally. He has been invited as keynote speaker for various university workshops, industrial sharings, conferences and tech meetups. During his spare time, he is also a Kaggler Master, and has won various competitions in the top 1% spots.
Weimin is currently the Data Science Lead at Go-Jek, and he is responsible for several Deep Learning initiatives such as Real-time Time Series Forecast, Fraud Detection and Food Recommendation. He is passionate about creating business impact using AI, with current focuses on Deep Learning as well as TensorFlow in production. In his free time, he also loves to share his knowledge by publishing blogs.