## Topic outline

• ### Intelligent Systems for Pattern Recognition - 9 CFU

Code: 760AA, Credits (ECTS): 9, Semester: 2, Official Language: English

Instructor: Davide Bacciu

Contact: email - phone 050 2212749

Office: Room 367, Dipartimento di Informatica, Largo B. Pontecorvo 3, Pisa

Office Hours: (email to arrange meeting)

Supporting InstructorAntonio Carta (email)

• ### Course Information

#### Weekly Schedule

The course is held on the second term. The tentative schedule for A.A. 2021/22 is provided in table below.

The first lecture of the course will be on Tuesday 15/02/2022. The course will be hybrid, both in person and online on the dedicated MS Team.

Recordings of the lectures will be made available to the students following the course.

Day Time
Tuesday 14.15-16.00 (Room D1, Teams meeting)
Wednesday 16.15-18.00 (Room A1, Teams meeting)
Thursday 14.15-16.00 (Room A1, Teams meeting)

#### Course Prerequisites

Course prerequisites include knowledge of machine learning fundamentals (e.g. covered through ML course). Knowledge of elements of probability and statistics, calculus and optimization algorithms are also expected. Previous programming experience with Python is a plus for the practical lectures.

#### Course Overview

The course introduces students to the analysis and design of advanced machine learning and deep learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques.

The course is articulated in five parts. The first part introduces basic concepts and algorithms concerning traditional pattern recognition, in particular as pertains sequence and image analysis. The next two parts introduce advanced models from two major learning paradigms, that are deep neural networks and generative models, and their use in pattern recognition applications. The fourth part will introduce fundamentals of reinforcement learning and deep reinforcement learning. The final part of the course will present selected recent works, models and applications of learning models.

Presentation of the theoretical models and associated algorithms will be complemented by introductory classes on the most popular software libraries used to implement them.

The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models.

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.

Topics covered -Bayesian learning, graphical models, learning with sampling and variational approximations, fundamentals of deep learning (CNNs, AE, DBN, GRNs), deep learning for machine vision and signal processing, advanced deep learning models (transformers, VAE, GANs, NTMs), deep graph networks, reinforcement learning and deep reinforcement learning, signal processing and time-series analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bio-informatics, robotics, medical imaging, etc), introduction to programming libraries and frameworks.

#### Textbooks and Teaching Materials

The course does not have an official textbook covering all its contents. However, a list of reference books covering parts of the course is listed at the bottom of this section (note that all of them have an electronic version freely available online).

[BRML] David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press (PDF)

[DL] Ian Goodfellow and Yoshua Bengio and Aaron Courville , Deep Learning, MIT Press (ONLINE)

[RL] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Second Edition, MIT Press, 2018 (PDF)

• ### Introduction (2h)

Introduction to the course philosophy, its learning goals and expected outcomes. We will discuss prospectively the overall structure of the course and the interelations between its parts. Exam modalities and schedule are also discussed.
Date Topic  References
1 15/02/2022
(14-16)
Introduction to the course
Motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications

• ### Fundamentals of Pattern Recognition (6h)

The module will provide a brief introduction to classical pattern recognition for signal/timeseries and for images. We will cover approaches working on the spatial (temporal) and frequency (spectral) domain, presenting methods to represent temporal and visual information in static descriptors, as well as approaches to identify relevant patterns in the data (feature descriptors). Methodologies covered include correlation analysis, Fourier analysis, wavelets, intensity gradient-based descriptors and detectors, normalized cut segmentation.

Date Topic  References
2
16/02/2022
(16-18)
Signal processing
Timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.

3 17/02/2022
(14-16)
Image Processing I
Spatial feature descriptors (color histograms, SIFT); spectral analysis.
[1] Survey on visual descriptors
4 22/02/2022
(14-16)
Image Processing II
Feature detectors (edge, blobs); image segmentation; wavelet decompositions
[2] Survey on visual feature detectors

Software

A wavelet browser to visualize some popular wavelet families and their instances, powered by the PyWavelet library.

• ### Generative Learning (20h)

The module introduces probabilistic learning, causal models, generative modelling and Bayesian learning. We will discuss fundamental algoritms and concepts, including Expectation-Maximization, sampling and variational approximations, and we will study relevant models from the three fundamental paradigms of probabilistic learning, namely Bayesian networks, Markov networks and dynamic models.  Models covered include: Bayesian Networks, Hidden Markov Models, Markov Random Fields, Boltzmann Machines,  Latent topic models.

Date Topic  References
5
23/02/2022
(16-18)
Introduction to Generative and Graphical Models.
P
robability refresher; graphical model representation; directed and undirected models
[BRML] Ch. 1 and 2 (Refresher)
[BRML] Sect. 3.1, 3.2 and 3.3.1
(conditional independence)
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
6
24/02/2022
(14-16)
Conditional independence and causality - Part I
Bayesian networks; Markov networks; 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)

7
01/03/2022
(14-16)
Conditional independence and causality - Part II
d-separation; structure learning in Bayesian Networks
[BRML] Sect. 9.5.1 (PC algorithm)
[BRML] Sect. 9.5.2 (Independence testing)
[BRML] Sect. 9.5.3 (Structure scoring)
[3] A short review of BN structure learning
[4] PC algorithm with consistent ordering for large scale data
[5] MMHC - Hybrid structure learning algorithm

Software
- A selection of BN structure learning libraries in Python: pgmpy, bnlearn, pomegranate.
- bnlearn: the most consolidated and efficient library for BN structure learning (in R)
- Causal learner: a mixed R-Matlab package integrating over 26 BN structure learning algorithms.

8
02/03/2022
(16-18)
Hidden Markov Models  - Part I
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)
[6]  A classical tutorial introduction to HMMs
9
03/03/2022
(14-16)
Hidden Markov Models - Part II
EM algorithm, learning as inference, Viterbi Algorithm
[BRML] Sect. 23.2.6 (Viterbi)
[BRML] Sect. 23.3.1-23.3.4 (EM and learning)

10
08/03/2022
(14-16)
HMM III + 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)
[7,8] Two comprehensive tutorials on CRF ([7] more introductory and [8] more focused on vision)
[9] A nice application of CRF to image segmentation

Sofware
Check out pgmpy: it has Python notebooks to introduce to working with MRF/CRF
11
09/03/2022
(16-18)
Bayesian Learning I
Principles of Bayesian learning; EM algorithm objective; principles of variational approximation
[BRML] Sect. 11.2.1 (Variational EM)
12
10/03/2022
(14-16)
Bayesian Learning II
latent topic models; Latent Dirichlet Allocation; machine vision application of latent topic models
[10] LDA foundation paper
[11] A gentle introduction to latent topic models
[12] Foundations of bag of words image representation
Sofware
13
14/03/2022
(11-13)
ROOM C1
Bayesian Learning III
sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methods

Guest lecture by Daniele Castellana

[13] A step-by-step derivation of collapsed Gibbs sampling for LDA
14
15/03/2022
(14-16)
Boltzmann Machines
bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence
[14] 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.

• ### Deep Learning (24h)

The module presents the fundamental concepts, challenges, architectures and methodologies of deep learning. We introduce the learning of neural representations from vectorial, sequential and image data, covering both supervised and unsupervised learning, and hinting at various forms of weak supervision. We close the gap between neural networks and probabilistic learning by discussing generative deep learning models. Models covered include: deep autoencoders, convolutional neural networks, long-short term memory, gated recurrent units, deep reservoir computing, sequence-to-sequence, neural attention, neural Turing machines, variational autoencoders, generative adversarial networks. Methodological lectures will be complemented by introductory seminars to Keras-TF and Pytorch.

Date Topic  References
15
16/03/2022
(16-18)
Convolutional Neural Networks I
Introduction to the deep learning module; i
ntroduction to CNN; basic CNN elements
[DL] Chapter 9
[15-19] Original papers for LeNet, AlexNet, VGGNet, GoogLeNet and ResNet.
16
17/03/2022
(14-16)
Convolutional Neural Networks II
CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code
[20] Complete summary of convolution arithmetics
[21] Seminal paper on batch normalization
[22] CNN interpretation using deconvolutions
22/03/2022
(14-16)
LECTURE CANCELLED (RECOVERED ON THE 14th MARCH)

23/03/2022
(16-18)
LECTURE CANCELLED (RECOVERED ON THE 25th MARCH)
24/03/2022
(14-16)
LECTURE CANCELLED (RECOVERED ON THE 28th MARCH)
17  25/03/2022
(14-16)
ROOM D1 + ONLINE
Deep Autoencoders
S
parse, denoising and contractive AE; deep RBM
[DL] Chapter 14, Sect 20.3, 20.4.0 (from 20.4.1 onwards not needed)  Additional Readings
[24] DBN: the paper that started deep learning
[25] Deep Boltzmann machines paper
[26] Review paper on deep generative models
[27] Long review paper on autoencoders from the perspective of representation learning
[28] Paper discussing regularized autoencoder as approximations of likelihood gradient
18  28/03/2022
(16-18)
ROOM C
Gated Recurrent Networks I
Deep learning for sequence processing; gradient issues;
[30] Original LSTM paper
[31] An historical view on gated RNN
[32] Gated recurren units paper
[33] Seminal paper on dropout regularization
19  29/03/2022
(14-16)
Gated Recurrent Networks II
long-short term memory; gated recurrent units; generative use of RNN
Sofware
20 30/03/2022
(16-18)
Coding practice I - Tensorflow

21 31/03/2022
(14-16)
Coding practice II - PyTorch

22  05/04/2022
(14-16)
Deep Randomized Networks - Guest lecture by Claudio Gallicchio
reservoir computing; randomized models; echo state networks

23  06/04/2022
(16-18)
sequence-to-sequence;  attention models; multiscale network; hierarchical models
[34,35] Models of sequence-to-sequence and image-to-sequence transduction with attention
[36,37] Models optimizing dynamic memory usage (clockwork RNN, zoneout)
[38] Transformer networks: a paper on the power of attention without recurrence
24  07/04/2022
(14-16)
Neural Reasoning
memory networks; neural Turing machines
[39] Differentiable memory networks
[40,41] Neural Turing Machines and follow-up paper on pondering networks
25  12/04/2022
(14-16)
Unsupervised and Generative Deep Learning I
explicit distribution models; neural ELBO; variational autoencoders
[42] PixelCNN - Explict likelihood model
[43] Tutorial on VAE

Sofware
26  13/04/2022
(16-18)
Unsupervised and Generative Deep Learning II

[DL] Section 20.10.4
[44] Tutorial on GAN (here another online resource with GAN tips)
[45] Wasserstein GAN
[46] Tutorial on sampling neural networks
[47] Progressive GAN
[48] Cycle Gan
[49] Seminal paper on Adversarial AEs

Sofware

• ### Reinforcement Learning (14h)

We formalise the reinforcement learning problem by rooting it into Markov decision processes and we provide an overview of the main approaches to design reinforcement learning agents, including model-based, model-free, value and policy learning. We link classical approaches with modern deep learning based approximators (deep reinforcement learning). We overview the main programming frameworks available. Methodologies covered include: dynamic programming, MC learning, TD learning, SARSA, Q-learning, deep Q-learning, policy gradient and deep policy gradient, MC tree search.

Date Topic  References
28 21/04/2022
(14-16)
Reinforcement learning fundamentals
reinforcement learning problems; environment; agent; actions and policies; taxonomy of approaches
[RL] Chapter 1 Software
Open AI gym for RL environments and tasks
29 26/04/2022
(14-16)
Markov Decision Processes
formal model of RL probelms; rewards; returns; Bellman expectation and optimality
[RL] Chapter 3
30 27/04/2022
(14-16)
Model-Based Planning
dynamic programming; policy evaluation; policy iteration; value iteration
[RL] Chapter 4
Software
Dynamic programming demo on Gridworld in Javascript (with code)
31 28/04/2022
(14-16)
Model-free reinforcement learning
model-free predition; model-free control; Monte Carlo methods; TD learning;  SARSA; Q-learning

[RL] Section 5.1-5.6, 6.1-6.6, 7.1, 7.2, 12.1, 12.2, 12.7
[50] The original Q-learning paper

Software:

03/05/2022
(14-16)
NO LECTURE
04/05/2022
(16-18)
NO LECTURE
32 05/05/2022
(14-16)
Value-function Approximation
linear incremental methods; batch value function approximation; deep Q-learning; linear least-squares control
[51] Original DQN paper
[52] Double Q-learning
[53] Dueling Network Architectures
[54] Prioritized Replay
33 09/05/2022
(16-18 - ROOM C
[55] Original REINFORCE paper
[56] Learning with the actor-critic architecture
[57] Accessible reference to natural policy gradient
[58] A3C paper
[60] TRPO paper
[61] PPO paper
34 10/05/2022
(14-16)
[62] UCT paper: the introduction of Monte-Carlo planning
[63] MoGo: the grandfather of AlphaGo (RL using offline and online experience)
[64] AlphaGo paper
[65] AlphaGo without human bootstrap

• ### Advanced Topics and Applications (8h)

The module covers some recent and interesting development and research topics in the field of machine learning. Topics choice is likely to vary at each edition. Example topics include: deep learning for graphs, learning with structured data, continual learning, distributed learning, learning-reasoning integration, edgeAI,etc.. The module concludes with a final lecture which discusses the course content retrospectively and details the exam modalities, topics and deadlines.

 35 11/05/2022(16-18) Deep learning for graphs   Software- PyDGN: our in-house DLG library- PyTorch geometric- Deep graph libraryAdditional readings[66-67] Seminal works on neural networks for graphs[68] Recent tutorial paper Date Topic References Additional Material 27 20/04/2022(16-18) Continual Learning - Guest lecture by Vincenzo Lomonaco 36 12/05/2022(14-16) Final lecture 37 20/05/2022(11-13) ROOM D1 Research seminars by Ph.D. studentsDraft programme:Andrea Cossu - The reasonable effectiveness of pre-trained models in Continual LearningMichele Resta - Continual Incremental Language Learning for Neural Machine TranslationRIccardo Massidda - Ontology-Driven Semantic Alignment of Artificial NeuronsDanilo Numeroso - Neural Algorithmic ReasoningFrancesco Landolfi - Graph Pooling with Maximum Weight k-Independent Sets

• ### Course Grading and Exams

Typical course examination (for students attending the lectures) is performed in 2 stages: midterm assignments and an oral exam. Midterms waive the final project.

#### Midterm Assignment

Midterms consist in short assignments involving with one of the following tasks:

• A quick and dirty (but working) implementation of a simple pattern recognition algorithm
• A report concerning the experience of installing and running a demo application realized using available deep learning and machine learning libraries
• A summary of a recent research paper on topics/models related to the course content.

The midterms can consist in either the delivery of code (e.g. colab notebook) or a short slide deck (no more than 10 slides) presenting the key/most-interesting aspects of the assignment.

Students might be given some amount of freedom in the choice of assignments, pending a reasonable choice of the topic. The assignments will roughly be scheduled every 3/4 weeks.

#### Oral Exam

The oral examination will test knowledge of the course contents (models, algorithms and applications).

The final exam vote is given by the oral grade. The midterms only wave the final project but do not contribute to the grade. In other words you can only fail or pass a midterm. You need to pass all midterms in order to succesfully wave the final project.

#### Alternative Exam Modality (No Midterms / Non attending students)

Working students, those not attending lectures, those who have failed midterms or simply do not wish to do them, can complete the course by delivering a final project and an oral exam.  Final project topics will be released in the final weeks of the course: contact the instructor by mail to arrange choice of the topics once these are published.

The final project concerns preparing a report on a topic relevant to the course content or the realization of a software implementing a non-trivial learning model and/or a PR application relevant for the course. The content of the final project will be discussed in front of the instructor and anybody interested during the oral examination. Students are expected to prepare slides for a 15 minutes presentation which should summarize the ideas, models and results in the report. The exposition should demonstrate a solid understanding of the main ideas in the report.

Grade for this exam modality is determined as

$$G = 0.5 \cdot (G_P + G_O)$$

where $$G_P \in [1,32]$$ is the project grade and $$G_O \in [1,30]$$ is the oral grade

• ### Bibliography

Bibliographic References

1. Scott Krigg, Interest Point Detector and Feature Descriptor Survey, Computer Vision Metrics, pp 217-282, Open Access Chapter
2. Tinne Tuytelaars and Krystian Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, Vol. 3, No. 3 (2007) 177–2, Online Version
3. C. Glymour, Kun Zhang and P. Spirtes, Review of Causal Discovery Methods Based on Graphical Models Front. Genet. 2019, Online version
4. Bacciu, D., Etchells, T. A., Lisboa, P. J., & Whittaker, J. (2013). Efficient identification of independence networks using mutual information. Computational Statistics, 28(2), 621-646, Online version
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6. Lawrence R. Rabiner:a tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, pages 257-286, Online Version
7. Charles Sutton and Andrew McCallum,  An Introduction to Conditional Random Fields, Arxiv
8. Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction, Foundations and Trends in Computer Graphics and Vision, Online Version
9. Philipp Krahenbuhl, Vladlen Koltun, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Proc.of NIPS 2011, Arxiv
10. D. Blei, A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003
11. D. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012, Free Online Version
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14. Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Technical Report 2010-003, University of Toronto, 2010
15. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel. Handwritten digit recognition with a back-propagation network, Advances in Neural Information Processing Systems, NIPS, 1989
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22. M.D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, ICML 2013, Arxiv
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33. N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014
34. Bahdanau et al, Neural machine translation by jointly learning to align and translate, ICLR 2015, Arxiv
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36. Koutník et al, A Clockwork RNN, ICML 2014, Arxiv
37. Krueger, Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activation, ICLR 2018, Arxiv
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60. Schulman et al, Trust Region Policy Optimization, ICML, 2015, PDF
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