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2024　Machine Learning for Innovation

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Academic unit or major
Graduate major in Engineering Sciences and Design
Instructor(s)
Tanaka Masayuki 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Fri5-6(S2-204（S221）)  
Group
-
Course number
ESD.D508
Credits
1
Academic year
2024
Offered quarter
2Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

Machine learning is widely used in many applications, including autonomous vehicles, robotics, and medical diagnosis. Recognition of an image is one of the best examples of machine learning or artificial intelligence. Topics of this course include fundamental components of deep learning such as the convolution layer, full connection layer, pooling layer, ReLU layer, and a softmax layer. In this course, students develop and train their network with Pytorch by themselves.

Student learning outcomes

Students are expected to
(i) gain an ability to build and learn deep neural networks,
(ii) gain an ability to use numerical computing environments using PyTorch to solve engineering problems,
(iii) gain the practical skills to apply deep learning techniques such as momentum and data argumentation after taking this course.

Keywords

Object recognition, Convolutional neural network (CNN), Deep learning, PyTorch

Competencies that will be developed

✔ Specialist skills ✔ Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

This class is a kind of active learning. The instructor will give some information, but students are required to develop their PyTorch code.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Introduction
Class 2 Example of image classification Example of image classification
Class 3 Gradient decent approach Gradient decent approach
Class 4 Loss function Loss function
Class 5 Overfitting Overfitting
Class 6 Feature extraction and transfer learning Feature extraction and transfer learning
Class 7 Classification methods Classification methods
Class 8 Applications Applications

Out-of-Class Study Time (Preparation and Review)

This class is a kind of active learning. The instructor will give some information, but students are required to develop their PyTorch code.

Textbook(s)

None. Please google by related keywords.

Reference books, course materials, etc.

None. Please google by related keywords.

Assessment criteria and methods

Assignments and report

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students need to implement the PyTorch code by themselves.
Students who took the course of Image Recognition (＃SCE.I501) cannot take this course.

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