Lecture Calendar

Date Room Topic References Additional Material
1 19/02/2018
(14-16)
 C1 Introduction to the course: motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications




 2 20/02/2018
(14-16)		  C1 Signal processing: timeseries; time domain analysis (statistics, correlation); spectral analysis (fourier analysis; wavelets).
   Demo
Matlab temperature seasonality demo
 3  26/02/2018
(14-16)		  C1 Image processing: feature descriptors (color histograms, SIFT), spectral analysis, feature detectors (edge, blobs and segments).
   Additional Readings
[1,2] Two high-level surveys on visual feature extraction and representation
 4  27/02/2018
(14-16)		  C1 Generative and Graphical Models: probability refresher; graphical model representation; directed and undirected models: Bayesian networks; Markov networks; conditional independence and d-separation.
 [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, 4.2.0-4.2.2 (Undirected Models)
[BRML] Sect. 4.5 (Expressiveness)
 Sofware
  • Pyro - Python library based on PyTorch
  • PyMC3 - Python library based on Theano
  • Edward - Python library based on TensorFlow
 5 06/03/2018
(14-16)
  C1 Hidden Markov Models:  learning in directed graphical models; forward-backward algorithm;  generative models for sequential data
 [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 HMMs
 6 12/03/2018
(14-16)
  C1 Hidden Markov Models: part 2
    Sofware
  • hmmlearn - Scikit-learn like library for HMMs
  • hmms 0.1 - scalable discrete and continuous time HMMs in Python
 7 13/03/2018
(14-16)		  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
(14-16)
 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
(14-16)		 C1
Bayesian Learning: non-parametric models; variational learning; sampling
[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
 M1  22/03/2018
(11-13)		  N Midterm 1 discussion
    
10 26/03/2018
(14-16)		  C1 Generative models for structures
(guest seminar by Daniele Castellana)
   Additional Readings
[11] HMM extension to bottom-up trees
[12] Mixture of trees paper

Sofware
Official Python implementation for the bottom-up tree model
 11 27/03/2018
(14-16)		 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 half-semester exams   
(28/04/2018-13/04/2018) 

   Date Room
Topic
References
Additional Material
12  16/04/2018
(14-16)		 C1 Deep Autoencoders (part II) + Convolutional Neural Networks (part I):
deep RBM; introduction to CNN; basic CNN elements
 [DL] Chapter 9 Additional Readings
[18-22] 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
(11-13)
 N Learning in Structured Domain I: Recursive Neural Networks
(Guest lecture by Alessio Micheli)

15 23/04/2018
(14-16)		  C1 Reservoir Computing Approaches
(Guest lecture by Claudio Gallicchio)
    
16 24/04/2018
(14-16)		  C1 Learning in Structured Domain II: Neural Networks for Graphs
(Guest lecture by Alessio Micheli)
    
 17 30/04/2018
(14-16)
  C1 Gated Recurrent Networks: deep learning for sequence processing; gradient issues; long-short term memory; gated recurrent units; generative use of RNN
 [DL] Sections 10.1-10.3, 10.5-10.7, 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 (11-13)
 N Midterm 2 discussion    

 07/05/2018 (14-16)  C1 Lecture cancelled due to students' assembly
   

08/05/2018 (14-16)  C1 Lecture cancelled due to students' elections
    
  18 10/05/2018 (11-13)  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 (14-16)  C1 PyTorch – neural networks in Python:
python; pytorch
(Coding practice by Antonio Carta)
    
 20 15/05/2018 (14-16)  C1 Advanced Recurrent Architectures: sequence-to-sequence;  attention models; multiscale network; memory networks; neural reasoning.
  [DL] Sections 10.12, 12.4.5
 Additional Readings
[32,33] Models of sequence-to-sequence and image-to-sequence transduction with attention
[34,35] Models optimizing dynamic memory usage (clockwork RNN, zoneout)
[36] Differentiable memory networks
[37,38] Neural Turing Machines and follow-up paper on pondering networks
 
 21 21/05/2018 (14-16)  C1 Generative and Unsupervised Deep Learning: explicit distribution models; variational autoencoders; adversarial learning.
  [DL] Sections 20.9, 20.10.1-20.10.4
 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 (14-16)  C1 Solving 'omics problems with Deep Learning
(guest seminar by Marco Podda)
    
 23 24/05/2018 (11-13)
  B Fooling Deep Nets for fun and profit: On the state of Adversarial Machine Learning
(guest seminar by Francesco Crecchi)
    
 24 28/05/2018 (14-16)  C1 Memory Enhanced Networks:
attention, Neural Turing Machine, Linear Memory Network
(Guest seminar by Antonio Carta)
    
 25 29/05/2018 (14-16)  C1 Final lecture: course wrap-up; research themes; final projects; exam modalities
    
 M3 01/06/2018 (11-13)  N1 Midterm 3 discussion

Bibliography

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  4. Charles Sutton and Andrew McCallum,  An Introduction to Conditional Random Fields, Arxiv
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Last modified: Wednesday, 20 February 2019, 11:56 PM