**Quora Answer Sean McClure**

**Linear Algebra**

**Calculus**

**Probability**

**Optimization**

- Heuristics
- Iterative Methods

This is further broken down, as I show below, by the following subtopics that are

**prominent in machine learning.****BUT**…these topics are best learned

*in context*. Please do not jump into these topics without first building a real-world application (if you’re a

**Data Scientist**) or at least some functioning code (if you’re a

**researcher**). Machine learning success doesn’t happen by memorizing a knowledge-base of fundamentals devoid of real-world context. Since you mentioned

*for research*, you can use the latter approach.

Begin by writing code that solves a problem, and use these mathematical concepts

**when and where they can help you**. This will ensure you learn how these concepts help in real situations.
(current topics submitted by the machine learning community)

**Linear Algebra**

**Calculus**

**Probability**

**Optimization**

**Heuristics**

**Iterative methods**

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