Lesson 1
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.
Fundamentals of Probabilistic Graphical Models
Course
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.
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.
3 weeks
Real-world Projects
Completion Certificate
Last Updated January 18, 2022
No experience required
Course Lessons
Lesson 2
Probability
Sebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.
Lesson 3
Spam Classifier with Naive Bayes
In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.
Lesson 4
Bayes Nets
Sebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.
Lesson 5
Inference in Bayes Nets
Sebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.
Lesson 6
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.
Lesson 7
Dynamic Time Warping
Thad explains the Dynamic Time Warping technique for working with time-series data.
Lesson 8 • Project
Project: Part of Speech Tagging
In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.
Taught By The Best
Sebastian Thrun
Founder and Executive Chairman, Udacity
As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
Thad Starner
Professor of Computer Science, Georgia Tech
Thad Starner is the director of the Contextual Computing Group (CCG) at Georgia Tech and is also the longest-serving Technical Lead/Manager on Google's Glass project.
Taught By The Best
Sebastian Thrun
Founder and Executive Chairman, Udacity
As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
Thad Starner
Professor of Computer Science, Georgia Tech
Thad Starner is the director of the Contextual Computing Group (CCG) at Georgia Tech and is also the longest-serving Technical Lead/Manager on Google's Glass project.
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Fundamentals of Probabilistic Graphical Models