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 the image recognition course includes fundamental components of deep learning such as convolution layer, full connection layer, pooling layer, ReLU layer, and a softmax layer. In this course, students develop and train their network with matlab by themselves.
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 MATLAB to solve engineering problems,
(iii) gain practical skill to apply the deep learning techniques such as momentum, data arugumentation and filter setting, after taking this course.
✔ 該当する | 実務経験と講義内容との関連(又は実践的教育内容) |
---|---|
A faculty who has a private company experience gives a lecture. |
Object recognition, Convolutional neural network (CNN), Deep learning, matlab
✔ 専門力 | ✔ 教養力 | コミュニケーション力 | 展開力(探究力又は設定力) | ✔ 展開力(実践力又は解決力) |
This class is a kind of active learning. Instructor will give some information, but students are required to develop their matlab code.
授業計画 | 課題 | |
---|---|---|
第1回 | Introduction | Introduction |
第2回 | Example of image classification | Example of image classification |
第3回 | Gradient decent approach | Gradient decent approach |
第4回 | Loss function | Loss function |
第5回 | Overfitting | Overfitting |
第6回 | eature extraction and transfer learning | eature extraction and transfer learning |
第7回 | Classification methods | Classification methods |
第8回 | Applications | Applications |
学修効果を上げるため,教科書や配布資料等の該当箇所を参照し,「毎授業」授業内容に関する予習と復習(課題含む)をそれぞれ概ね100分を目安に行うこと。
None
None
Assignments and report
None
Students need to implement the matlab code by themselves.
Student who took the course of Computational Imaging (#SCE.I501) cannot take this course.