4 ECTS credits
120 h study time
Offer 1 with catalog number 4024399ENR for all students in the 2nd semester
at
a (E) Master - advanced level.
- Semester
- 2nd semester
- Enrollment based on exam contract
- Impossible
- Grading method
- Grading (scale from 0 to 20)
- Can retake in second session
- Yes
- Taught in
- English
- Partnership Agreement
- Under interuniversity agreement for degree program
- Faculty
- Faculteit Ingenieurswetenschappen
- Department
- Applied Physics and Photonics
- External partners
- Universiteit Gent
- Educational team
- Yunfeng Nie
Francesco Ferranti
(course titular)
- Activities and contact hours
- 24 contact hours Lecture
12 contact hours Seminar, Exercises or Practicals
- Course Content
- Introduction to supervised, unsupervised and reinforcement learning
- Interpolation and regression techniques
- Pre-processing techniques (e.g., smoothing, cosmic spike removal, baseline correction)
- Classification techniques
- Dimensionality reduction and clustering techniques
- Sampling techniques
- Modeling of multi-output complex frequency/wavelength-dependent response
- Electromagnetics, optics and biophotonics as application domains
- Introduction to these application domains and links among them
- Design and data analysis
- Practice examples (e.g., photonic sensors, optical imaging, biophotonic data classification.)
- Course material
- Handbook (Recommended) : Deep learning, Goodfellow - Bengio - Courville, The Mit Press, 2017
Handbook (Recommended) : The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009
Handbook (Recommended) : Kevin P. Murphy, "Machine Learning, a Probabilistic Perspective", MIT Press, 2012
Handbook (Recommended) : Christopher M. Bishop, “Pattern recognition and machine learning”, Springer, 2006
Handbook (Recommended) : Yaser Abu-Mostafa et al.,"Learning from data", AMLbook.com, 2012
Handbook (Recommended) : Scientific literature
Digital course material (Required) : Slides and course notes used during the course
- Additional info
N/A
- Learning Outcomes
-
General Learning Outcomes
Learning various concepts, methods, and key technologies relevant in the supervised and unsupervised machine learning areas. This knowledge is general purpose and students will practically apply it to a set of very relevant photonic applications, namely electromagnetic and optical designs, and data analysis for biophotonics. Optics will be presented as part of electromagnetics, showing connections between full-wave, diffractive and refractive (geometrical optics) solutions. Design can include data preparation, optimization and tolerance analysis as practical activities. Biophotonic data will be used for classification problems and the importance of pre-processing techniques will be shown (e.g., for Raman spectroscopy data). Electromagnetics and biophotonics connections will be shown for example when using nanostructures for Surface Enhanced Raman spectroscopy. Students will acquire insight, skills and experience in solving photonic/optical design and data analysis problems using machine learning techniques.
- Grading
-
The final grade is composed based on the following categories:
Oral Exam determines 33% of the final mark.
Practical Exam determines 33% of the final mark.
Other Exam determines 34% of the final mark.
Within the Oral Exam category, the following assignments need to be completed:
- oral exam
with a relative weight of 33
which comprises 33% of the final mark.
Note: oral exam on project (33,33%)
Within the Practical Exam category, the following assignments need to be completed:
- practical exam
with a relative weight of 33
which comprises 33% of the final mark.
Note: lab exercises + projects + homework (33,33%)
Within the Other Exam category, the following assignments need to be completed:
- other exam
with a relative weight of 34
which comprises 34% of the final mark.
Note: project assignment (33,33%)
- Additional info regarding evaluation
The final exam will be based on the evaluation of a project assignment and of an oral discussion on the project report. During semester: graded project reports; graded lab sessions; graded homework.
- Allowed unsatisfactory mark
- The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.
Academic context
This offer is part of the following study plans:
Master of Photonics Engineering: Standaard traject (only offered in Dutch)
Master of Photonics Engineering: On campus traject
Master of Photonics Engineering: Online/Digital traject