**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.

- 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

**Content (tentative - will change)**

**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

**Grading**

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)

**Tutorial:**

- TBA

Last update: 25th September 2023.