Information
We will meet for the first time on Monday October 2nd at 18:15 in the room A250 of Science Building A
(D3 (2) on the Nagoya University Campus map). There I will try to explain all details to the course.
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
The topics below are from the previous year. This year we plan to change the content a bit. If you have any suggestions or wishes please let me know!
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:
Last update: 25th September 2023.
We will meet for the first time on Monday October 2nd at 18:15 in the room A250 of Science Building A
(D3 (2) on the Nagoya University Campus map). There I will try to explain all details to the course.
- 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. (Not created yet)
- 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
The topics below are from the previous year. This year we plan to change the content a bit. If you have any suggestions or wishes please let me know!
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 programming in Python
- Overview of machine learning
- Supervised learning: Linear & logistic regression
- Generative Learning algorithms: Naive Bayes
- Reinforcement Learning: Q-Learning
- Unsupervised learning: k-means clustering
- Neural networks & Deep 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 2nd.
- Room: A250 (Computer room) in Science Building A (D3 (2) on the Nagoya University Campus map)
- TBA
Last update: 25th September 2023.