Introduction to Machine Learning Algorithms with a real world case study. Host: Kunal Singh, Machine Learning at UC Berkeley

About this STEMCasts® episode:

Get a grasp of Machine Learning algorithms using examples from the STEM-Away® forum. Don’t miss out on this exciting webinar, a learning resource for upcoming projects on our platform!

Meet Your Instructor:

This STEMCasts® is hosted by Kunal Singh, a Senior at UC Berkeley. Kunal is a member and officer of Machine Learning at Berkeley. He has a passion for Natural Language Processing (NLP), to get computer to process and analyze large amounts of language data. His most recent internship experience was at Primer AI where he designed, implemented, optimized, and productionized NLP-based algorithm for computational storytelling from news events.

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Please signup by indicating your preference below. You will receive a notification with meeting details within 2-4 days. The webinar will be recorded with a focus on the speaker. There will be an additional 15 mins recording at the end for students who wish to be included in the final video. The student interaction video will be a mix of Q&A and audience feedback.


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What You’ll Learn:

  • Fundamental skills necessary for Machine Learning
  • Popular machine learning algorithms
  • Introduction to deep learning
  • Deep Learning models - BERT, XLNET
  • Identifying and using attributes in models
  • Similarity models - Cosine similarity, KNN model
  • Collaborative filtering
  • Real world case study of applying machine learning algorithms to recommend topics & cluster tags in a forum


Target Audience:

  • High school students and college students interested in machine learning applications.
  • Students with a desire to learn about new algebra concepts and applications to computer engineering will benefit most from this event.


Background required:

  • Linear Algebra
  • Programming in Python
  • Coursera course in Machine Learning is helpful, but not required


STEMCasts® Schedule:

  1. Introduction and Overview of Machine Learning concepts
  2. Collaborative filtering
  3. Introduction to Deep learning
  4. Deep learning models
  5. Using attributes in models
  6. Application of Deep learning to recommend topics in Discourse


Reply to this topic with questions and feedback. This webinar is part of a STEM-Away® Pathway in Machine Learning.