**Mathematics for machine learning - Fall 2022**

(Special Mathematics Lecture)

**Detailed information will follow in the summer 2022.**

This course is targeted at any student of Nagoya University who is interested in some of the mathematics used in machine learning. As a background I expect that the students have some background in Linear Algebra and Calculus (e.g. Linear Algebra I & Calculus I from the G30 Program). If you have any questions or suggestions on the course please feel free to contact me.

**Content (tentative)**

Machine learning became a popular and really broad field in recent years. Machine learning algorithms are used in a wide variety of applications, such as email filtering, computer vision, medicine, language translation, computer games, economic, etc.. The goal of this course is to give a brief introduction into machine learning with a focus on the mathematical tools used.

We will probably cover following topics:

- Overview of machine learning
- (Linear) Regression
- Review Linear Algebra
- Programming & doing mathematics in Python
- Introduction to Probability
- Support vector machines
- k-means clustering
- Neural networks
- Deep learning

Last update: 23rd February 2022.