Deep Learning - CPGEI
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 (including Long-Short-Term Memory Networks - LSTM); Generative Adversarial Networks (GAN); Object Recognition (including You Only Look Once - YOLO); 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 (SYN) and asynchronous (ASYN) classes.
Grade: Assignments (40%) and Final Project (60%).
Lecturers: Heitor Silvério Lopes and André Eugenio Lazzaretti.
Collaborators (PhD students): Andrei Inácio, Matheus Gutoski, Anderson Brilhador, and Clayton Kossoski.
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
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.
Online book:
Michael Nielsen - Neural Networks and Deep Learning.
Misc:
Week 3 - October 12 - Holiday or Recess
Week 4 - October 19
Lecturer:
André e Matheus.
Content:
Assignments:
Week 6 - November 02 - Holiday or Recess
Week 11 - December 7
Lecturer:
Heitor e André.
Content:
(SYNC) Final Project and Assignment Discussions.
(ASYNC) Other Recent Topics (watch them if necessary for your work):
Assignments:
No assignments.
Week 12 - December 14
Lecturer:
Heitor e André.
Content:
(SYNC) Final Project and Assignment Discussions.
Final Project and Grades
Content:
Assignment Grades and Final Project Proposals (link).
Dates and rules:
Final Report Deadline: February 07.
Must contain: Introduction (detailing the addressed problem); Methodology; Results (with comparisons, if possible); Conclusions.
Suggested template: IEEE.
Presentation Deadline: 14-18 Feb.
15 minutes for each work + 5 minutes for questions.