General Information
Objective: Study of the main deep learning methods and their applications.
Syllabus: Machine learning basics; Deep Feedforward Networks; Convolutional Neural Networks (CNN), Recurrent Neural Networks; Generative Adversarial Networks (GAN); Object Detection; Segmentation; Natural Language Processing (NLP); Advanced models; Frameworks for deep learning and Practical Aspects; Applications of deep learning models for real-world problems.
Duration/credits: 45 hours/3 credits (12 weeks).
Time: Tuesdays (17:50 - 21:10) - online with synchronous online (SYN) and assynchronous (ASYN) classes.
Grade: Assignments (40%) and Final Project (60%).
Lecturers: Heitor Silvério Lopes and André Eugenio Lazzaretti.
Collaborators: Andrei Inácio, Matheus Gutoski, Anderson Brilhador, Marco A. S. Teixeira, and Clayton Kossoski.
Final Project and Grades
Assignment Grades (link).
Project Proposals - Guidelines:
Problem to be solved. Respond clearly: What is the problem? Preferably a real problem that can be solved via deep learning.
Dataset available? Few data may make it unfeasible.
Do you need HW (GPU)? The LABIC cluster may be made available.
What techniques do you intend to use and why?
If you have a comparison parameter (papers, kaggle, etc).
Final Project - Rules:
It can be in pairs.
Something more than getting data and network already available is expected.
Important to detail and interpret the results.
Important: it could become a publication in a conference/journal.
Final Project - Presentation:
Final Report Deadline: February 13. Must contain: Introduction (detailing the addressed problem); Methodology; Results (with comparisons, if possible); Conclusions. Suggested template: IEEE.
Presentation Deadline: 20-24 Feb. 15 minutes for each work + 5 minutes for questions.
Bibliography and Support Materials
Books:
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning. MIT Press, 2016.
Chollet, F., Deep Learning with Python. Manning, 2018
Raschka, S. Python Machine Learning. Packt, 2020
Michael Nielsen - Neural Networks and Deep Learning.
Other Courses:
Andrew Ng at Coursera.
Sebastian Raschka at UW Madison.
Fei Fei Li et al. at Stanford (most similar).
Hugo Larochelle at Université de Sherbrooke.
Deep Unsupervised Learning at UC Berkeley.
Introduction to Deep Learning at MIT.
Week 1 - September 27
Lecturer:
Heitor e André.
Content:
Assignments:
Week 6 - November 01
Lecturer:
Heitor e André.
Content:
(SYNC) Final Project and Assignment Discussions.
Week 12 - December 20
Lecturer:
Heitor e André.
Content:
(SYNC) Final Project and Assignment Discussions.