Academic Year 201718
Lecture Calendar
Date  Room  Topic  References  Additional Material 


1  19/02/2018 (1416) 
C1  Introduction to the course: motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications 

2  20/02/2018 (1416) 
C1  Signal processing: timeseries; time domain analysis (statistics, correlation); spectral analysis (fourier analysis; wavelets). 
Demo Matlab temperature seasonality demo 

3  26/02/2018 (1416) 
C1  Image processing: feature descriptors (color histograms, SIFT), spectral analysis, feature detectors (edge, blobs and segments). 
Additional Readings [1,2] Two highlevel surveys on visual feature extraction and representation 

4  27/02/2018 (1416) 
C1  Generative and Graphical Models: probability refresher; graphical
model representation; directed and undirected models: Bayesian
networks; Markov networks; conditional independence and dseparation. 
[BRML] Ch. 1 and 2 (Refresher) [BRML] Sect. 3.1, 3.2 and 3.3.1 (conditional independence) [BRML] Sect. 3.3 (Directed Models) [BRML] Sect. 4.1, 4.2.04.2.2 (Undirected Models) [BRML] Sect. 4.5 (Expressiveness) 
Sofware 
5  06/03/2018 (1416) 
C1  Hidden Markov Models: learning in directed graphical models; forwardbackward algorithm; generative models for sequential data 
[BRML] Sect. 23.1.0 (Markov Models) [BRML] Sect. 23.2.023.2.4 (HMM and forward backward) 
Additional Readings [3] A classical tutorial introduction to HMMs 
6  12/03/2018 (1416) 
C1  Hidden Markov Models: part 2 
Sofware


7  13/03/2018 (1416) 
C1  Markov Random Fields: learning in undirected graphical models; conditional random fields; pattern recognition applications 
[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/CRF 
8  19/03/2018 (1416) 
C1 
Boltzmann Machines: bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence 
[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. 
9  20/03/2018 (1416) 
C1 
Bayesian Learning: nonparametric models; variational learning; sampling 
[BRML] Sect. 11.2.1 (Variational EM), 20.420.6.1 (LDA) 
Additional Readings [8] LDA foundation paper [9] A gentle introduction to latent topic models [10] Foundations of bag of words image representation Sofware

M1  22/03/2018 (1113) 
N  Midterm 1 discussion 

10  26/03/2018 (1416) 
C1  Generative models for structures (guest seminar by Daniele Castellana) 
Additional Readings [11] HMM extension to bottomup trees [12] Mixture of trees paper Sofware Official Python implementation for the bottomup tree model 

11  27/03/2018 (1416) 
C1 
Deep Autoencoders: introduction to the deep learning module, sparse, denoising and contractive AE; deep RBM. 
[DL] Chapter 14 
Additional Readings [13] DBN: the paper that started deep learning [14] Deep Boltzmann machines paper [15] Review paper on deep generative models [16] Long review paper on autoencoders from the perspective of representation learning [17] Paper discussing regularized autoencoder as approximations of likelihood gradient 
Lecture break period due to Easter holidays and halfsemester exams (28/04/201813/04/2018) 

Date  Room 
Topic 
References 
Additional Material 


12  16/04/2018 (1416) 
C1  Deep Autoencoders (part II) + Convolutional Neural Networks (part I): deep RBM; introduction to CNN; basic CNN elements 
[DL] Chapter 9  Additional Readings [1822] Original papers for LeNet, AlexNet, VGGNet, GoogLeNet and ResNet. 
13  17/04/2018 
C1 
Convolutional Neural Networks (part II): CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code 
[DL] Chapter 9 
Additional Readings [23] Complete summary of convolution arithmetics [24] Seminal paper on batch normalization [25] CNN interpretation using deconvolutions 
14  19/04/2018 (1113) 
N  Learning in Structured Domain I: Recursive Neural Networks (Guest lecture by Alessio Micheli) 

15  23/04/2018 (1416) 
C1  Reservoir Computing Approaches (Guest lecture by Claudio Gallicchio) 

16  24/04/2018 (1416) 
C1  Learning in Structured Domain II: Neural Networks for Graphs (Guest lecture by Alessio Micheli) 

17  30/04/2018 (1416) 
C1  Gated Recurrent Networks: deep learning for sequence processing; gradient issues; longshort term memory; gated recurrent units; generative use of RNN 
[DL] Sections 10.110.3, 10.510.7, 10.10, 10.11 
Additional Readings [26] Paper describing gradient vanish/explosion [27] Original LSTM paper [28] An historical view on gated RNN [29] Gated recurren units paper [30] Seminal paper on dropout regularization [31] Tree LSTM for sentiment analysis using classical recursive encoding Sofware

M2  03/05/2018 (1113) 
N  Midterm 2 discussion  
07/05/2018 (1416)  C1  Lecture cancelled due to students' assembly 

08/05/2018 (1416)  C1  Lecture cancelled due to students' elections 

18  10/05/2018 (1113)  B  From TensorFlow to Keras: python; tensorflow; keras; CNN (Coding practice by Francesco Crecchi) 
Software Coding examples from the lecture available on the group's GitLab 

19  14/05/2018 (1416)  C1  PyTorch – neural networks in Python: python; pytorch (Coding practice by Antonio Carta) 

20  15/05/2018 (1416)  C1  Advanced Recurrent Architectures: sequencetosequence; attention models; multiscale network; memory networks; neural reasoning. 
[DL] Sections 10.12, 12.4.5 
Additional Readings [32,33] Models of sequencetosequence and imagetosequence transduction with attention [34,35] Models optimizing dynamic memory usage (clockwork RNN, zoneout) [36] Differentiable memory networks [37,38] Neural Turing Machines and followup paper on pondering networks 
21  21/05/2018 (1416)  C1  Generative and Unsupervised Deep Learning: explicit distribution models; variational autoencoders; adversarial learning. 
[DL] Sections 20.9, 20.10.120.10.4 
Additional Readings [39] PixelCNN  Explict likelihood model [40] Tutorial on VAE [41] Tutorial on GAN (here another online resource with GAN tips) [42] A recent stabler extension to GAN [43] Tutorial on sampling neural networks Sofware

22  22/05/2018 (1416)  C1  Solving 'omics problems with Deep Learning (guest seminar by Marco Podda) 

23  24/05/2018 (1113) 
B  Fooling Deep Nets for fun and profit: On the state of Adversarial Machine Learning (guest seminar by Francesco Crecchi) 

24  28/05/2018 (1416)  C1  Memory Enhanced Networks: attention, Neural Turing Machine, Linear Memory Network (Guest seminar by Antonio Carta) 

25  29/05/2018 (1416)  C1  Final lecture: course wrapup; research themes; final projects; exam modalities 

M3  01/06/2018 (1113)  N1  Midterm 3 discussion 
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Last modified: Wednesday, 20 February 2019, 11:56 PM