Fundamentals of Probabilistic Graphical Models

Learn to use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions. Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in natural language processing, and more.

• Introduction to Probabilistic Models
• Welcome to Fundamentals of Probabilistic Graphical Models. In this lesson, we will cover the course overview, prerequisites, and do a brief introduction to probability.
• Probability
• Sebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.
• Spam Classifier with Naive Bayes
• In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.
• Bayes Nets
• Sebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.
• Inference in Bayes Nets
• Sebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.
• Part of Speech Tagging with HMMs
• Learn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.
• Dynamic Time Warping
• Thad explains the Dynamic Time Warping technique for working with time-series data.
• Project: Part of Speech Tagging
• In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.