Section outline
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The official language of the course is English: all materials, references and books are in English.
Lecture slides will be made available here, together with suggested readings.
Date Room Topic References Additional Material 1 18/02/2021
(14-16)ONLINE Introduction to the course: motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications
(slides)2 19/02/2021
(11-13)ONLINE Signal processing: timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.
(slides)3 25/02/2021
(14.00-15.30)ONLINE Image processing: feature descriptors (color histograms, SIFT), spectral analysis, feature detectors (edge, blobs and segments).
(slides)Additional Readings
[1,2] Two high-level surveys on visual feature extraction and representation4 26/02/2021
(11-13)ONLINE Generative and Graphical Models - Part 1: probability refresher; graphical model representation; directed and undirected models
(slides)[BRML] Ch. 1 and 2 (Refresher)
[BRML] Sect. 3.1, 3.2 and 3.3.1
(conditional independence)5 04/03/2021
(14-16)ONLINE Generative and Graphical Models - Part 2: Bayesian networks; Markov networks; conditional independence and d-separation. [BRML] Sect. 3.3 (Directed Models)
[BRML] Sect. 4.1, 4.2.0-4.2.2 (Undirected Models)
[BRML] Sect. 4.5 (Expressiveness)Software - Pyro - Python library based on PyTorch
- PyMC3 - Python library based on Theano
- Edward - Python library based on TensorFlow
- TensorFlow Probability - Probabilistic models and deep learning in Tensorflow
05/03/2021
(11-13)NO LECTURE DUE TO GRADUATION COMMITTEE (WILL BE RECOVERED) 6 11/03/2021
(14-16)ONLINE Hidden Markov Models - Part 1: learning in directed graphical models; forward-backward algorithm; generative models for sequential data
(slides)[BRML] Sect. 23.1.0 (Markov Models)
[BRML] Sect. 23.2.0-23.2.4 (HMM and forward backward)Additional Readings
[3] A classical tutorial introduction to HMMs7 12/03/2021
(11-13)ONLINE Hidden Markov Models - Part 2: EM algorithm, learning as inference, Viterbi Algorithm [BRML] Sect. 23.2.6 (Viterbi)
[BRML] Sect. 23.3.1-23.3.4 (EM and learning)8 18/03/2021
(14-16)ONLINE Markov Random Fields: learning in undirected graphical models; conditional random fields; pattern recognition applications
(slides)[BRML] Sect. 4.2.2, 4.2.5 (MRF)
[BRML] Sect. 4.4 (Factor Graphs)
[BRML] Sect. 5.1.1 (Variable Elimination and Inference on Chain)
[BRML] Sect. 9.6.0, 9.6.1, 9.6.4, 9.6.5 (Learning in MRF/CRF)Additional Readings
[4,5] Two comprehensive tutorials on CRF ([4] more introductory and [5] more focused on vision)
[6] A nice application of CRF to image segmentation
Sofware
Check out pgmpy: it has Python notebooks to introduce to working with MRF/CRF9 19/03/2021
(11-13)ONLINE Boltzmann Machines: bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence
(slides)[DL] Sections 20.1 and 20.2 Additional Readings
[7] A clean and clear introduction to RBM from its author
Sofware
Matlab code for Deep Belief Networks (i.e. stacked RBM) and Deep Boltzmann Machines.10 25/03/2021
(14-16)ONLINE Bayesian Learning: non-parametric models; variational learning
(slides)[BRML] Sect. 11.2.1 (Variational EM), 20.4-20.6.1 (LDA) Additional Readings
[8] LDA foundation paper
[9] A gentle introduction to latent topic models
[10] Foundations of bag of words image representation
Sofware- A didactic Matlab demo of bag-of-words for images
- A standalone Matlab toolbox for latent topic models (including LDA examples, but discontinued) and the official Matlab LDA implementation
- A chatty demo on BOW image representation in Python
- Yet another Python implementation of image BOW
11 26/03/2021
(11-13)ONLINE Bayesian Learning: sampling methods - Guest lecture by Daniele Castellana
(slides)[BRML] Sect. 27.1-27.3, 27.4.1, 27.6.2 M1 30/03/2021
(13.30-16.00)ONLINE Midterm 1 discussions 12 08/04/2021
(14-16)ONLINE Convolutional Neural Networks (part I): introduction to CNN; basic CNN elements [DL] Chapter 9 Additional Readings
[11-15] Original papers for LeNet, AlexNet, VGGNet, GoogLeNet and ResNet.13 09/04/2021
(11-13)ONLINE Convolutional Neural Networks (part II): CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code
(slides)[DL] Chapter 9 Additional Readings
[16] Complete summary of convolution arithmetics
[17] Seminal paper on batch normalization
[18] CNN interpretation using deconvolutions14 15/04/2021
(14-16)ONLINE Deep Autoencoders: introduction to the deep learning module, sparse, denoising and contractive AE; deep RBM
(slides)[DL] Chapter 14, Sect 20.3, 20.4.0 (from 20.4.1 onwards not needed) Additional Readings
[19] DBN: the paper that started deep learning
[20] Deep Boltzmann machines paper
[21] Review paper on deep generative models
[22] Long review paper on autoencoders from the perspective of representation learning
[23] Paper discussing regularized autoencoder as approximations of likelihood gradient15 16/04/2021
(11-13)ONLINE Gated Recurrent Networks: deep learning for sequence processing; gradient issues; long-short term memory; gated recurrent units; generative use of RNN
(slides)[DL] Sections 10.1-10.3, 10.5-10.7, 10.10, 10.11 Additional Readings
[24] Paper describing gradient vanish/explosion
[25] Original LSTM paper
[26] An historical view on gated RNN
[27] Gated recurren units paper
[28] Seminal paper on dropout regularization
Sofware- A fun and high-level introduction to generative use of LSTM
- The up-to-date implementation of NeuraTalk
16 22/04/2021
(14-16)ONLINE Gated Recurrent Networks II 17 23/04/2021
(11-13)ONLINE Deep randomized neural networks - Guest lecture by Claudio Gallicchio
(slides)18 29/04/2021
(14-16)ONLINE Learning in Structured Domain I: Recursive Neural Networks - Guest lecture by Alessio Micheli
(slides)19 30/04/2021
(11-13)ONLINE Learning in Structured Domain II: Neural Networks for Graphs - Guest lecture by Alessio Micheli
(slides)M2 03/05/2021
(13.30-17)ONLINE Midterm 2 discussions 20 06/05/2021
(14-16)ONLINE An introduction to Tensorflow and Keras: python; numpy, tensorflow, keras;
(Coding practice by Federico Errica)
(slides)21 07/05/2021
(11-13)ONLINE PyTorch – neural networks in Python: python; pytorch; RNN
(Coding practice by Antonio Carta)
(slides)22 13/05/2021
(14-16)ONLINE Advanced Recurrent Architectures: sequence-to-sequence; attention models; multiscale network; memory networks; neural reasoning.
(slides)[DL] Sections 10.12, 12.4.5 Additional Readings
[29,30] Models of sequence-to-sequence and image-to-sequence transduction with attention
[31,32] Models optimizing dynamic memory usage (clockwork RNN, zoneout)
[33] Differentiable memory networks
[34,35] Neural Turing Machines and follow-up paper on pondering networks
[36] Transformer networks: a paper on the power of attention without recurrence23 14/05/2021
(11-13)ONLINE Generative and Unsupervised Deep Learning: explicit distribution models; variational autoencoders; adversarial learning.
(slides)[DL] Sections 20.9, 20.10.1-20.10.4 Additional Readings
[37] PixelCNN - Explict likelihood model
[38] Tutorial on VAE
[39] Tutorial on GAN (here another online resource with GAN tips)
[40] Wasserstein GAN
[41] Tutorial on sampling neural networks
Sofware- A tutorial on VAE with code
- Official Wasserstein GAN code
- Pixel-CNN code
- A (long) list of GAN models with (often) associated implementation
24 20/05/2021
(14-16)ONLINE Continual learning. Guest lecture by Vincenzo Lomonaco 25 21/05/2021
(11-13)ONLINE Final lecture: course wrap-up; research themes; final projects; exam modalities
(slides)M3 07/06/2021
(10-13.30)ONLINE Midterm 3 discussions