MICRO-723 / 2 credits

Teacher(s): Borhani Navid, Psaltis Demetri

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: Next time: Spring 2022


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

Keywords

Deep learning, TensorFlow, Artificial neural networks, Imaging

Learning Prerequisites

Required courses

Proficiency in Python, basic optics

Recommended courses

MICRO-567 Optical Wave Proagation

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:

  • Choose A computational imaging model
  • Structure The database for training artificial neural networks
  • Implement Artifical neural networks using the TensorFlow machine learning library.

Teaching methods

1 hour/week  lecture 

1 hour/week interactive artificial neural network develoment for selected problems

 

 

 

 

Expected student activities

Attend lectures weekly

Attend exercise sessions

Participate in a class project 

Turn in homework every two weeks

 

 

 

 

Assessment methods

Homeworks

Project report

 

Resources

Bibliography

Tensor flow 

 

 

Notes/Handbook

Class notes will be posted on Moodle

In the programs

  • Exam form: During the semester (session free)
  • Subject examined: Deep Learning for Optical Imaging
  • Lecture: 1 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Practical work: 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: 1 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Practical work: 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: 1 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Practical work: 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: 1 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: 1 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: 1 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Practical work: 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: 1 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks

Reference week