## What do I need to know before this course?

Before taking this course, it is recommended to have familiarity with the basic probability theory and basic linear algebra [1]. For probability theory, having knowledge equivalent to Stat 116 would be sufficient [1]. As for linear algebra, any one of the following courses would provide more than enough preparation: Math 51, Math 103, Math 113, or CS 205 [1]. Additionally, it is assumed that students have basic knowledge of computer science, including understanding of concepts like big-O notation, data structures such as queues, stacks, and binary trees, and programming skills to write simple computer programs [3]. Most of the class will not be heavily programming intensive, and programming assignments will likely be done using either MATLAB or Octave [3].
Ask more:
- Is there a specific textbook recommended for this course?
- What are some examples of topics covered in probability theory?
- How is linear algebra used in machine learning?
References:
1. [Syllabus.pdf](https://utfs.io/f/7cbc532d-edc3-4e45-a190-d4278bbadb3f-ub7gg5.pdf)
2. [Lecture 1 - PDF Transcript.pdf](https://utfs.io/f/b0e14d33-81c7-4db8-9202-c7b9145557eb-ixpqa0.pdf)
3. [Lecture 1 - PDF Transcript.pdf](https://utfs.io/f/b0e14d33-81c7-4db8-9202-c7b9145557eb-ixpqa0.pdf)