This page will be updated in September 2024. Enjoy your summer break until then 😎.
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). I do not expect that the students can already program, but if you do not have any Python experience you might need to do a basic course by yourself at the beginning (or in the summer break). If you took the Data Science Exercise B then this course is perfect for you. If you have any questions or suggestions on the course please feel free to contact me.
Materials
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 at least the following topics:
The grade will we based on homework submissions and one final project.
The credits follow the new rule for SML classes. (SML=Special mathematics lecture)
Please contact me via email if you have any questions.
Course schedule (tentative)
Lecture:
- If you are Japanese student, please also register at the NU-EMI project for this course.
- We will use a Discord server for communication and for sharing materials & code.
- We will use TACT for homework submission.
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). I do not expect that the students can already program, but if you do not have any Python experience you might need to do a basic course by yourself at the beginning (or in the summer break). If you took the Data Science Exercise B then this course is perfect for you. If you have any questions or suggestions on the course please feel free to contact me.
Materials
- Coming soon
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 at least the following topics:
- Introduction to machine learning and programming in Python
- Supervised learning: Linear & Logistic regression, Neural networks
- Unsupervised learning: PCA, Autoencoder
- Reinforcement Learning: Q-Learning
The grade will we based on homework submissions and one final project.
The credits follow the new rule for SML classes. (SML=Special mathematics lecture)
Please contact me via email if you have any questions.
Course schedule (tentative)
Lecture:
- Time: Monday 6th period (18:15-19:45). First lecture: October 7th.
- Room: A250 (Computer room) in Science Building A (D3 (2) on the Nagoya University Campus map)
- Time: TBA
- Room: A250 (Same as the lecture)
Last update: 18th August 2024.