Introduction to Representation Learning

Representation learning has emerged as a crucial technique in machine learning for discovering the underlying structure and patterns in data. This technique involves transforming raw data into meaningful features or representations, which can then be used for a variety of tasks such as classification, clustering, and prediction.

In this event, we will provide an in-depth introduction to representation learning, covering its fundamental principles, classical algorithms, and modern deep learning techniques.

Agenda:

1. Most fundamental way to represent structured data
2. Using classical ML algorithms to generate representations
3. Deep learning to generate representations
4. Complex deep learning models for representing text, image and graph
5. Advanced model frameworks to represent any arbitrary data

Speaker: Ashutosh Hathidara

Currently pursuing MS in CS from Indiana University. He has worked on multiple ML domains like Recommendation Engines, Computer Vision, Graph ML, Explainable AI etc. He is a former ML intern & upcoming ML Engineer at TikTok. He runs a Youtube Channel called DevSence

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