Bayesian Networks in Swift [Integrated Course]
Swift is a new, general-purpose, multi-paradigm, and compiled programming language developed by Apple Inc. for iOS, macOS, watchOS, tvOS, and Linux.
This course presents how to implement optimized data structures for structure learning and inference for Bayesian Networks. The examples used in this course are very practical and grounded with examples from real-world data.
This course begins with Swift-implemented exercises using Bayesian Networks. You will learn various algorithms such as genetic, hill climbing, and EM.
You will gradually progress to learn Swift-implemented Bayesian Network applications. In this topic, you will understand concepts such as MSR’s TrueSkill algorithm, turbo codes, and decision-theoretic troubleshooting. Finally, you will learn the concept of speech recognition with HMMs.
By the end of this course, you will see how to leverage the tools Swift provides to help you build AI systems really fast, scalable, and easy to reason about.
Key Features
- Explore structure learning implementation
- Learn Swift-implemented Bayesian Network structure learning algorithms
- Understand Swift-implemented Bayesian Network applications
- Take 25 assessments specific to the course
- Create a mini-project by the end of this course
Who this course is for
This course is for developers who are familiar with the Swift language and Bayesian Network concepts. If you want to build scalable AI systems using Bayesian Networks in Swift, then this course is for you.
- Publication date:
- August 2017
- Publisher
- Packt
- Duration
- 2 hours
- ISBN
- 9781788398046