Friday Hacks #75, September 12
Posted on by Joey
Learn about machine learning this week! Shamraz will share about using ML in geography and Shawn will show you how to use ML with scikit-learn.
Date/Time: Friday, September 12 at 6:30pm
Venue: SR4, Town Plaza (Level 2), University Town, NUS
Free pizza is served before the talks.
Machine Learning and Environmental Modelling
Intelligence is increasingly associated with computers, but historically computers derived their power from blind obedience and speed. What is this artificial intelligence and ‘big data’ that we hear so much about? How do machine learning techniques like neural networks and decision trees work?
In the second part, Shamraz discusses the use of machine learning in geography and how they differ from the alternative: process-based conceptual models. He focuses on his research on a river in the mountains of northern thailand. Shamraz will show how he uses machine learning to fill gaps in river flow data using environmental data such as spatial rainfall and how wet the soil is.
Growing up, Shamraz was fascinated by computers. A Microsoft Certified Professional at the age of 11, he finished his degree in Computer Science at the NUS School of Computing in 2009, before his interest in people and interdisciplinary work led him to geography. Shamraz studies political geography, artificial intelligence for environmental modeling and the importance of experiential learning through overseas fieldwork. Shamraz currently teaches at Tembusu College, NUS where he helped design the algorithm used to generate classlists that are diverse in terms of faculty and gender.
Machine Learning with scikit-learn
Now that you have a brief introduction of what machine learning is, Shawn will show you how you can use it. Using example data from the Titanic, he will walk you through building AI models to predict whether a passenger will survive using scikit, an open source machine learning library for python.
Shawn is an NUS Hackers alumnus, currently working as a research assistant in the NUS speech recognition group. He previously worked as a data janitor at Semantics3 in San Francisco.
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