Course Outline

Module 1: Introduction to Deep Unsupervised Learning

  • Motivation for representation learning and refresher on neural networks 
  • Compare shallow vs. deep learning and deep learning vs. classical machine learning
  • Explore use cases of deep unsupervised learning today


Module 2: Semi-supervised Learning

  • Intro to automatic feature extraction and autoencoders, including a comparison of autoencoders to dimensionality reduction and an overview of complete, undercomplete, and overcomplete autoencoders
  • Intro to semi-supervised learning using autoencoders and how supervised and unsupervised learning complement each other
  • Develop semi-supervised fraud detection application using autoencoders
  • Compare the unsupervised, supervised, and semi-supervised solutions and evaluate results


Module 3: Generative Modeling

  • Intro to generative modeling, including restricted Boltzmann machines (RBMs), deep belief networks (DBNs), and generative adversarial networks (GANs)
  • Deep dive into GANs, including how a generator and a discriminator work together to produce synthetic data
  • Frame how generative modeling and GANs fit into the overall space of unsupervised learning
  • Demonstration of GANs in action using code to produce synthetic data

Instructor's Bio: Ankur Patel

Ankur Patel is the co-founder & Head of Data at Glean, an AI-powered spend intelligence solution for managing vendor spend, and the co-founder of Mellow, a fully managed machine learning platform for SMBs. He is an applied machine learning specialist in both unsupervised learning and natural language processing, and he is the author of Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data and Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand. Prior to founding Glean and Mellow, Ankur led data science and machine learning teams at several startups including 7Park Data, ThetaRay, and R-Squared Macro and was the lead emerging markets trader at Bridgewater Associates. He is a graduate of Princeton University and currently resides in New York City.

Who will be interested in this course?

  • Python coding experience

  • Familiarity with pandas, numpy, and scikit-learn

  • Understanding of basic machine learning concepts, including supervised learning

  • Experience with deep learning and frameworks such as TensorFlow or PyTorch is a plus