• 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.

この講座は機械学習に用いられる数学の入門です。授業は英語で行われます。この科目に興味のある人や英語で行われる授業に参加したい人は、名古屋大学の学生であれば誰でも大歓迎です。この講座の受講する場合、線形代数と微積分の基礎的な内容に慣れている方が望ましいですが、最初の講義でPythonを基礎から学びますのでプログラミングの知識は不要です。この講座の履修登録はNU EMI​で済ませてください。英語に不安のある学生には、日本人のTAによるサポートもあります。

​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
Semester projects
- here some of the students projects will be displayed -

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
    ​
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: 
  • Time: See the calender below (Alternating Thursday (6th period) - Wednesday (5th period) - Friday (6th period) )
  • Room: A250 (Same as the lecture)


​The following gives a tentative overview of the topics we will cover each week. 
Week 01 (10/02-10/08): ​Introduction & Linear regression I
Week 02 (10/09-10/15): No lecture on monday 9th (sports day)
Week 03 (10/16-10/22): Linear regression II
Week 04 (10/23-10/29): Logistic regression 
Week 05 (10/30-11/05): Neural Networks I: Basics 
Week 06 (11/06-11/12): Neural Networks II: Using tensorflow
Week 07 (11/13-11/19): Neural Networks III: Backpropagation 
Week 08 (11/20-11/26):  Neural Networks IV: Backpropagation II & Convolutional Neural Networks
Week 09 (11/27-12/03): Reinforcement learning & Q-learning I
Week 10 (12/04-12/10): Q-learning II
Week 11 (12/11-12/17): Deep Q-learning 
Week 12 (12/18-12/24): k-means clustering, Christmath Quiz 
Week 13 (12/25-12/31): 🎅🎄 No Lecture and Tutorial in this week 🎅🎄
☃️ Winter Vacation (12/28-01/09) ☃️ 
Week 14 (01/08-01/14): No lecture on Monday 8th January (still winter vacation)
Week 15 (01/15-01/21): Principal Component Analysis I
Week 16 (01/22-01/28): Principal Component Analysis II & Autoencoders
Week 17 (01/29-02/06): Semester projects

Last update: 29th January 2024.