2020 Image Recognition

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Systems and Control Engineering
Instructor(s)
Tanaka Masayuki 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Fri5-6(S611)  
Group
-
Course number
SCE.I501
Credits
1
Academic year
2020
Offered quarter
2Q
Syllabus updated
2020/9/18
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 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.

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 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.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
A faculty who has a private company experience gives a lecture.

Keywords

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

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. Instructor will give some information, but students are required to develop their matlab code.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction of course, cifar-10, and sample network, GUI To run the sample network
Class 2 One hot encoding, softmax, MSEloss, KL-divergence, cross-entropy loss To compare MSE loss and cross-entropy
Class 3 Convolution and activation, ReLU, LReLU, PReLU To compare ReLU, LReLU and PReLU
Class 4 Pooling layer and dropout To compare different parameter of dropout
Class 5 Merge layer and batch normalization To try Batch normalization
Class 6 Optimization and data augumentation To compare sgd and adam
Class 7 Parameter tuning 1 To try parameter tuning
Class 8 Parameter tuning 2 To try parameter tuning

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

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None

Reference books, course materials, etc.

None

Assessment criteria and methods

Assignments and report

Related courses

  • SCE.I531 : Computer Vision

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

None

Other

Students need to implement the matlab code by themselves.
Student who took the course of Computational Imaging (#SCE.I501) cannot take this course.

Page Top