2024 Faculty Courses School of Engineering Department of Information and Communications Engineering Graduate major in Engineering Sciences and Design
Machine Learning for Innovation
- Academic unit or major
- Graduate major in Engineering Sciences and Design
- Instructor(s)
- Masayuki Tanaka
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Fri
- Class
- -
- Course Code
- ESD.D508
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (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.
Evaluation methods and criteria
Assignments and report
Related courses
- None
Prerequisites
Students need to implement the PyTorch code by themselves.
Students who took the course of Image Recognition (＃SCE.I501) cannot take this course.