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Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Course Objectives:

  • Identify why deep learning is currently popular
  • Optimize and evaluate models using loss functions and performance metrics
  • Mitigate common problems that arise in machine learning
  • Create repeatable and scalable training, evaluation, and test datasets

Syllabus

  1. Introduction
  2. Practical ML
  3. Optimization
  4. Generalization and Sampling
  5. Summary

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