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Introduction to Semi-Supervised Learning
Semi-Supervised learning is a relatively new approach to working with data that does not come from canonical and well-prepared sources. In practice, it is very rare to come across data that has class labels readily and abundantly available to satisfactorily train a classifier. This is when semi-supervised learning techniques can be beneficial by synthetically applying labels to unlabeled data by leveraging the underlying statistical distributions. In this talk, you will hopefully walk away with the following: - Deeper understanding to semi-supervised learning - Introduction to several different algorithms such as CPLE & S3VM - Demonstration of semi-supervised learning in Notebooks Robert Chong is the Vice-Chair of the local Austin ACM chapter. He has a very deep interesting in machine learning and is always learning (when he's not spending time with his family). https://www.linkedin.com/in/robertjchong Agenda: 6:30 Food + Networking 7:00 Presentation + QA Location: TBD - Check back later RSVP: • Seating is limited to the first 75 to RSVP. • Please let me know if you have any questions about RSVP.

data.world

7000 N. Mopac Expy Suite 425 · Austin, TX

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Austin Association of Computing Machinery (ACM) Special Interest Group in Knowledge Discovery and Data Mining (SIGKDD). Local Austin chapter of ACM SIGKDD, the premier professional society for machine learning and data mining. This group is specialized to Hadoop based big data machine learning.

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