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:
Last update: 23rd February 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.