Deep learning
EE-559 / 4 credits
Teacher: Cavallaro Andrea
Language: English
Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.
Summary
This course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.
Content
This course equips students with a comprehensive foundation of modern deep learning, enabling students to design and train discriminative and generative neural networks for a wide range of tasks. Topics include:
- Deep learning applications (natural language processing, computer vision, audio processing, biology, robotics, science), principles and regulations
- Loss functions, data and labels, data provenance
- Training models: gradients and initialization
- Generalization and performance
- Transformers
- Graph neural networks
- Generative adversarial networks
- Variational autoencoders
- Diffusion models
- Multi-modal models
- Interpretability, explanations, bias and fairness
Keywords
machine learning, neural networks, deep learning, python
Learning Prerequisites
Required courses
- Basics in probabilities and statistics
- Linear algebra
- Differential calculus
- Python programming
Recommended courses
- Basics in optimization
- Basics in algorithmic
- Basics in signal processing
Important concepts to start the course
Discrete and continuous distributions, normal density, law of large numbers, conditional probabilities, Bayes, PCA, vector, matrix operations, Euclidean spaces, Jacobian, Hessian, chain rule, notion of minima, gradient descent, computational costs, Fourier transform, convolution.
Learning Outcomes
By the end of the course, the student must be able to:
- Interpret the performance of a deep learning model
- Analyze the limitations of a deep learning model
- Justify the choices for training and testing a deep learning model
- Propose new solutions for a given application
Transversal skills
- Respect relevant legal guidelines and ethical codes for the profession.
- Take account of the social and human dimensions of the engineering profession.
- Design and present a poster.
- Make an oral presentation.
- Demonstrate the capacity for critical thinking
Teaching methods
Ex-cathedra lectures, class discussion, exercises (using python), group project.
Expected student activities
Attendance to lectures, participation in discussions, completing exercises, completing a project, reading written material (scientific papers and books).
Assessment methods
Excercises and group project.
Resources
Références suggérées par la bibliothèque
Notes/Handbook
Not mandatory: http://www.deeplearningbook.org/
Websites
Moodle Link
In the programs
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Number of places: 150
- Exam form: During the semester (summer session)
- Subject examined: Deep learning
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
Reference week
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Légendes:
Lecture
Exercise, TP
Project, labs, other