Deep learning for optical imaging
MICRO-573 / 3 credits
Teacher: Psaltis Demetri
Language: English
Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.
Summary
This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source TensorFlow machine learning library developed by Google for Python.
Content
After a brief introduction to deep neural networks, the course will focus on the use and functionality of TensorFlow, and how it can be used to build models of different complexity for different types of optical imaging applications. Models will range from simple linear regression to convolutional neural networks (CNN) for image classification and mapping. The course will be assessed through coursework and group projects where the students will apply TensorFlow to specific machine learning applications.
Keywords
Deep learning, TensorFlow, Artificial neural networks, Imaging
Learning Prerequisites
Required courses
Proficiency in Python, basic optics
Recommended courses
MICRO-421 Imaging Optics
Important concepts to start the course
Python familiarity, linear systems, basic optics
Learning Outcomes
By the end of the course, the student must be able to:
- Implement
- Choose
- Demonstrate
- Apply
Teaching methods
2 hours/week lecture
1 hour/week interactive artificial neural network develoment for selected problems
In the programs
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Exam form: During the semester (summer session)
- Subject examined: Deep learning for optical imaging
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other