Course program
Introduction to deep learning, including the relation of deep learning and machine learning, and applications to computer vision and natural language processing.
Introduction of digital image processing, including convolutions, and introduction of text and speech input processing.
Revision of multinomial logistic regression for multi-class classification, linear classifiers, optimization with gradient descent and regularization.
Primer on the computational graph and backpropagation.
Convolutional Neural Networks and modern convolutional architectures.
Training ConvNets: Activation functions, data pre-processing, weight initialization, Batch normalization, DropOut and data augmentation, Hyper-parameter tuning, transfer learning.
Convolutional neural networks for time series.
Recurrent neural networks (RNN) and LSTMS.
Transformer networks, including attention and self-attention, positional encoding, the modern architectures of Transformer Networks and BERT.
Generative Deep Learning: auto-regressive generation, auto-encoders (AE), generative adversarial networks (GAN).
Ethics of Deep Learning.
Prerequisites
Students are expected to have knowledge of: 1) Python programming and bases of programming in Pytorch 2) Design of algorithms and data structures 3) General mathematics, algebra, set theory.
Books
Slides and coding scripts will be distributed after lectures, as well as references to online material, including papers and blogs.
Reference books Machine Learning:
Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/)
Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning
Reference books for Deep Learning:
Aston Zhang, Zachary Lipton, Alexander J. Smola, Mu Li, 2023. Dive Into Deep Learning (available at: https://d2l.ai/)
Francois Fleuret, 2024. The Little Book of Deep Learning. (available at: https://fleuret.org/dlc/)
Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković, 2024. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (available at: https://geometricdeeplearning.com/book/)
Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/)
Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/)
Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html)
Reference books for Computer Vision:
Antonio Torralba, Phillip Isola and William T. Freeman, 2024. Foundations of Computer Vision (https://mitpress.mit.edu/9780262048972/foundations-of-computer-vision/)
Richard Szeliski, 2010. Computer Vision: Algorithms and Applications
(available at: http://szeliski.org/Book)
Reference books for Robotics:
Frank Dellaert, 2024, Robotics. (available at: https://www.roboticsbook.org/)
Reference book for Python and Pytorch:
Aston Zhang, Zachary Lipton, Alexander J. Smola, Mu Li, 2023. Dive Into Deep Learning (available at https://d2l.ai/ , select Notebooks/Pytorch)
Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook)
Online tutorials for Python: https://docs.python.org/3/tutorial/
Online tutorials for Pytorch: https://pytorch.org/tutorials/
Frequency
While attendance is not mandatory, it is highly recommended to ensure you acquire the knowledge at the same pace as the lectures and to be best prepared for the written exam.
Exam mode
The exam is a written test with multiple-choice questions on theory and on exercises that cover the entire course programme.
Bibliography
Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning
Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković, 2024. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (available at: https://geometricdeeplearning.com/book/)
Frank Dellaert, 2024, Robotics. (available at: https://www.roboticsbook.org/)
Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/)
Francois Fleuret, 2024. The Little Book of Deep Learning. (available at: https://fleuret.org/dlc/)
Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/)
Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html)
Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/)
Antonio Torralba, Phillip Isola and William T. Freeman, 2024. Foundations of Computer Vision (https://mitpress.mit.edu/9780262048972/foundations-of-computer-vision/)
Aston Zhang, Zachary Lipton, Alexander J. Smola, Mu Li, 2023. Dive Into Deep Learning (available at: https://d2l.ai/)
Richard Szeliski, 2010. Computer Vision: Algorithms and Applications
(available at: http://szeliski.org/Book)
Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook)
Lesson mode
The course follows a traditional lecture format to lay the foundation for key concepts and their real-world applications. Students then actively engage in practical exercises, both in class and as homework, to solidify their understanding.
