Sebastian Thrun
Founder and Executive Chairman, Udacity
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.
- Course
- Advanced
- 14 hours
- Updated: Jun 19, 2024
Subscription · Monthly
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- Unlimited access to hundreds of top-rated courses
- Hands-on projects with expert feedback
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Skills you'll learn
10 skills
- Likelihood function
- Bayesian networks
- Part of speech tagging
- Basic probability
- Ibm watson
Prerequisites
7 prerequisites
Prior to enrolling, you should have the following knowledge:
- Scripting
- Jupyter notebooks
- Basic data structures and algorithms
- Basic descriptive statistics
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Course Outline
- 8 lessons
- 1 project
Program Instructors
2 instructors
Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:
Thad Starner
Professor of Computer Science, Georgia Tech
Sebastian Thrun
Founder and Executive Chairman, Udacity
Thad Starner
Professor of Computer Science, Georgia Tech
Subscription · Monthly
- Cancel Anytime
- Unlimited access to hundreds of top-rated courses
- Hands-on projects with expert feedback
- Personalized career coaching and interview prep
- Program Certificates