**Information**

- If you are
**Japanese student,**please also register at the NU-EMI project for this course. **Please join the NUCT Course for this class. We will use NUCT for the homework submission.**- We will use a Discord server for communication and for sharing materials & code.
- This course is a "Special Mathematics Lecture", which is an optional subject. It does not count towards the number of credits required for graduation in any program at Nagoya University. But students can get a grade for this course which can show up in their final transcript.
- Registration Code: 0061621

****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 of course programming experience can be helpful). If you have any questions or suggestions on the course please feel free to contact me.

**Materials**

- Lecture notes will be created during the course -

**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 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**

This course can not be used to get credits for graduation in any program at Nagoya University, but still, students can obtain a final grade that can show up in their final transcript. This grade will be based on submitted homework assignments, which will be a mix of programming and math exercises. Please sign up for the NUCT course if you want to submit homework.

**Course schedule**

**(tentative)**

**Lecture**:

- Time: Monday 6th period (18:15-19:45)
**First Lecture on Monday 3rd October 2022**- Room: A250, 2nd floor, Science A building (Computer room)

The following gives a tentative overview of the topics we will cover each week.

Week 01 (10/03-10/09):

****Introduction to machine learning and Python

Week 02 (10/10-10/16): No class (

**体育の日). Sports day: Please do 100 pushups instead.**

Week 03 (10/17-10/24): TBD

Week 04 (10/24-10/30): TBD

Week 05 (10/31-11/06): TBD

Week 06 (11/07-11/13): TBD

Week 07 (11/14-11/20): TBD

Week 08 (11/21-11/27): TBD

Week 09 (11/28-12/04): TBD

Week 10 (12/04-12/11): TBD

Week 11 (12/11-12/18): TBD

Week 12 (12/19-12/25): TBD

**Winter Vacation (12/28-01/07)**🎅🎄☃️ (Maybe no lecture on December 26th 2022)

Week 13 (01/09-01/15): Coming-of-age Day (Maybe no lecture on January 9th 2023)

Week 14 (01/16-01/22): TBD

Week 15 (01/23-01/29): TBD

Week 16 (01/30-02/06): TBD

Last update: 20th September 2022.