Resources for Getting Started With Probability in Machine Learning
Last Updated on September 25, 2019
Machine Learning is a field of computer science concerned with developing systems that can learn from data.
Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty.
Many aspects of machine learning are uncertain, including, most critically, observations from the problem domain and the relationships learned by models from that data. As such, some understanding of probability and tools and methods used in the field are required by a machine learning practitioner to be effective. Perhaps not initially, but certainly in the long run.
In this post, you will discover some of the key resources that you can use to learn about the parts of probability required for machine learning.
After reading this post, you will know:
- References that you can use to discover topics on probability.
- Books, chapters, and sections that cover probability in the context of machine learning.
- A division between foundational probability topics and machine learning methods that leverage probability.
Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.