Topic outline

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)

Weekly Schedule
The course is held on the second term. The tentative schedule for A.A. 2022/23 is provided in table below.
The first lecture of the course will be ON FEBRUARY 21st 2023 h. 11.30 (note the slightly delayed starting time for only this lecture). 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 11.0012.45 (Room C1, Online) Wednesday 16.1518.00 (Room E, Online) Thursday 14.1516.00 (Room C, Online) Objectives
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, their combinaton 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, NTMs), generative deep learning (VAE, GANs, diffusion models, scorebased models) deep graph networks, reinforcement learning and deep reinforcement learning, signal processing and timeseries analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bioinformatics, 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 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 Additional Material 1 21/02/2023
(11.3013)Introduction to the course
Motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applicationsRemember to fill in your contact details here (by 24/02)

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 gradientbased descriptors and detectors, normalized cut segmentation.
Date Topic References Additional Material 2 22/02/2023
(1618)Signal processing
Timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.3 23/02/2023
(1416)Image Processing I
Spatial feature descriptors (color histograms, SIFT); spectral analysis.Additional readings
[1] Survey on visual descriptors
Software: A tweakable and fast implementation of SIFT in C (on top of OpenCV)
4 01/03/2023
(1618)Image Processing II
Feature detectors (edge, blobs); image segmentation; wavelet decompositionsAdditional readings
[2] Survey on visual feature detectors
A reference book for the pattern recognition part is " S. THEODORIDIS, K. KOUTROUMBAS, Pattern Recognition, 4th edition". It is not needed for the sake of the course, but it is a reference book if you are interested on the topic. It is not available online for free (legally; what you do with Google is none of my business).
You can find the original NCUT paper freely available from authors here.
Software
A wavelet browser to visualize some popular wavelet families and their instances, powered by the PyWavelet library.
 A tweakable and fast implementation of SIFT in C (on top of OpenCV)

The module introduces probabilistic learning, causal models, generative modelling and Bayesian learning. We will discuss fundamental algoritms and concepts, including ExpectationMaximization, 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.
Bayesian Learning III
sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methodsDate Topic References Additional Material 5 02/03/2023
(1416)Introduction to Generative and Graphical Models.
Probability 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 03/03/2023
(1416)
AULA E
RECOVERY LECTUREConditional independence and causality  Part I
Bayesian networks; Markov networks; conditional independence;[BRML] Sect. 3.3 (Directed Models)
[BRML] Sect. 4.1, 4.2.04.2.2 (Undirected Models)
[BRML] Sect. 4.5 (Expressiveness)7 07/03/2023
(1113)Conditional independence and causality  Part II
dseparation; 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)Additional readings
[3] A short review of BN structure learning
[4] PC algorithm with consistent ordering for large scale data
[5] MMHC  Hybrid structure learning algorithm
If you are interested in deepening of your knowledge on causality this is an excellent book (also freely available online): Jonas Peters, Dominik Janzing, Bernhard Schölkopf, Elements of causal inference : foundations and learning algorithms, MIT Press.
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 RMatlab package integrating over 26 BN structure learning algorithms.8 08/03/2023
(1618)Hidden Markov Models  Part I
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)9 09/03/2023
(1416)Hidden Markov Models  Part II
EM algorithm, learning as inference, Viterbi Algorithm[BRML] Sect. 23.2.6 (Viterbi)
[BRML] Sect. 23.3.123.3.4 (EM and learning)Additional Readings
[6] A classical tutorial introduction to HMMs10 14/03/2023
(1113)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
[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/CRF11 15/03/2023
(1618)Bayesian Learning I
Principles of Bayesian learning; EM algorithm objective; principles of variational approximationBRML] Sect. 11.2.1 (Variational EM) 12 16/03/2023
(1416)Bayesian Learning II
latent topic models; Latent Dirichlet Allocation; machine vision application of latent topic models[BRML] Sect. 20.420.6.1 (LDA) Additional Readings
[10] LDA foundation paper
[11] A gentle introduction to latent topic models
[12] Foundations of bag of words image representation
Sofware
 A didactic Matlab demo of bagofwords 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
13 21/03/2023
(1113)Bayesian Learning III
sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methods[BRML] Sect. 27.127.3, 27.4.1, 27.6.2 Additional Readings
[13] A stepbystep derivation of collapsed Gibbs sampling for LDA14 22/03/2023
(1618)Boltzmann Machines
bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence[DL] Sections 20.1 and 20.2 Additional Readings
[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.

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/generative 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, longshort term memory, gated recurrent units, deep reservoir computing, sequencetosequence, neural attention, neural Turing machines, variational autoencoders, generative adversarial networks. diffusion models, scorebased methods. Methodological lectures will be complemented by introductory seminars to KerasTF and Pytorch.
Date Topic References Additional Material 15 23/03/2023
(1416)Convolutional Neural Networks I
Introduction to the deep learning module; introduction to CNN; basic CNN elements[DL] Chapter 9 Additional Readings
[1519] Original papers for LeNet, AlexNet, VGGNet, GoogLeNet and ResNet.16 28/03/2023
(1113)Convolutional Neural Networks II
CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code[DL] Chapter 9 Additional Readings
[20] Complete summary of convolution arithmetics
[21] Seminal paper on batch normalization
[22] CNN interpretation using deconvolutions
[23] CNN interpretation with GradCAM17 29/03/2023
(1618)Deep Autoencoders
Sparse, 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 gradient18 30/03/2023
(1416)Gated Recurrent Networks I
Deep learning for sequence processing; gradient issues;[DL] Sections 10.110.3, 10.510.7, 10.10, 10.11 Additional Readings
[29] Paper describing gradient vanish/explosion
[30] Original LSTM paper
[31] An historical view on gated RNN19 04/04/2023
(1113)Coding practice I  PyTorch
Guest lecture by Danilo Numeroso20 05/04/2023
(1618)Coding practice I  Tensorflow
Guest lecture by Valerio De Caro06/04/2023
11/04/2023NO LECTURE: Easter Break 21 12/04/2023
(1618)Gated Recurrent Networks II
longshort term memory; gated recurrent units; generative use of RNNAdditional Readings
[32] Gated recurren units paper
[33] Seminal paper on dropout regularization
Software A fun and highlevel introduction to generative use of LSTM
 The uptodate implementation of NeuraTalk
22 13/04/2023
(1416)Advanced Recurrent Architectures I
sequencetosequence; attention models;[DL] Sections 10.12, 12.4.5 Additional Readings
[34,35] Models of sequencetosequence and imagetosequence transduction with attention
[36] Transformer networks: a paper on the power of attention without recurrence23 14/04/2023
(1416)
AULA E
RECOVERY LECTUREAdvanced Recurrent Architectures II
multiscale network; hierarchical models; memory networks; neural Turing machinesAdditional Readings
[37,38] Models optimizing dynamic memory usage (clockwork RNN, zoneout)
[39] Differentiable memory networks
[40,41] Neural Turing Machines and followup paper on pondering networks28 02/05/2023
(1113)Reservoir Computing
Guest lecture by Andrea Ceni0304/05/2023 NO LECTURE 29 09/05/2023
(1113)Unsupervised and Generative Deep Learning I
explicit distribution models; neural ELBO; variational autoencoders[DL] Sections 20.9, 20.10.120.10.3 Additional Readings
[51] PixelCNN  Explict likelihood model
[52] Tutorial on VAE
Sofware30 10/05/2023
(1618)Unsupervised and Generative Deep Learning II
generative adversarial networks; adversarial autoencoders[DL] Section 20.10.4 Additional Readings
[53] Tutorial on GAN (here another online resource with GAN tips)
[54] Wasserstein GAN
[55] Tutorial on sampling neural networks
[56] Progressive GAN
[57] Cycle Gan
[58] Seminal paper on Adversarial AEs
Sofware Official Wasserstein GAN code
 A (long) list of GAN models with (often) associated implementation
31 11/05/2023
(1416)Unsupervised and Generative Deep Learning III
diffusion models; latent space diffusion; conditional diffusion models[59] Introductory and survey paper on diffusion models Additional Readings
[60] Seminal paper introducing diffusion models
[61] An intepretation of diffusion models as score matching
[62] Paper introducing the diffusion model reparameterization
[63] Diffusion beats GAN paper 
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 modelbased, modelfree, value and policy learning. We link classical approaches with modern deep learning based approximators (deep reinforcement learning). Methodologies covered include: dynamic programming, MC learning, TD learning, SARSA, Qlearning, deep Qlearning, policy gradient and deep policy gradient.
Date Topic References Additional Material 24 18/04/2023
(1113)Reinforcement learning fundamentals & ModelBased Planning
reinforcement learning problems; environment; agent; actions and policies; taxonomy of approaches; formal model of RL probelms; policy evaluation; policy iteration; value iteration[RL] Chapter 1, Chapter 3, Chapter 4 Software  Open AI gym for RL environments and tasks
 Dynamic programming demo on Gridworld in Javascript (with code)
25 19/04/2023
(1618)Modelfree reinforcement learning
modelfree predition; modelfree control; Monte Carlo methods; TD learning; SARSA; Qlearning[RL] Section 5.15.6, 6.16.6, 7.1, 7.2, 12.1, 12.2, 12.7 Additional reading:
[39] The original Qlearning paper26 20/04/2023
(1416)Valuefunction Approximation
linear incremental methods; batch value function approximation; deep Qlearning; linear leastsquares control[RL] Section 9.19.5, 9.8, 10.1, 11.111.5 Additional Reading:
[40] Original DQN paper
[41] Double Qlearning
[42] Dueling Network Architectures
[43] Prioritized Replay2527/04/2023 NO LECTURES 27 28/04/2023
(1416)
AULA E
RECOVERY LECTUREPolicy gradient & ActorCritic methods [RL] Chapter 13 Additional Reading:
[44] Original REINFORCE paper
[45] Learning with the actorcritic architecture
[46] Accessible reference to natural policy gradient
[47] A3C paper
[48] Deep Deterministic Policy Gradient
[49] TRPO paper
[50] PPO paper

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, continual learning, distributed learning, learningreasoning integration, edgeAI,etc.. The module concludes with a final lecture which discusses the course content retrospectively and details the exam modalities, topics and deadlines.
Date Topic References Additional Material 32 16/05/2023
(1113)Deep learning for graphs Software
 PyDGN: our inhouse DLG library
 PyTorch geometric
 Deep graph library
Additional readings
[6465] Seminal works on neural networks for graphs
[66] Recent tutorial paper33 17/05/2023
(1618)Research seminars
Algorithmic reasoning (Danilo Numeroso), continual learning (Andrea Cossu), graph reduction (Francesco Landolfi), causal learning (Riccardo Massidda), hyperparameters autotuning (Dario Balboni)34 18/05/2023
(1416)Final lecture 
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/mostinteresting 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).
Exam Grading (with Midterms)
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 nontrivial 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


Opened: Tuesday, 16 May 2023, 11:15 AMDue: Tuesday, 20 June 2023, 9:00 AM

Opened: Sunday, 21 May 2023, 11:15 AMDue: Friday, 30 June 2023, 9:00 AM

Opened: Sunday, 21 May 2023, 11:15 AMDue: Tuesday, 20 June 2023, 2:00 PM

Opened: Sunday, 21 May 2023, 11:15 AMDue: Friday, 7 July 2023, 2:00 PM

Opened: Friday, 1 September 2023, 11:15 AMDue: Tuesday, 5 September 2023, 2:00 PM

Exam Session I  30/05/2023 TIME PLACE NOTES 07/06/2023
h 09.3011.30Room X2
Polo FibonacciOral exam I 09/06/2023
h 09.3011.30Room X2
Polo FibonacciOral exam II Exam Session II  20/06/2023 TIME PLACE NOTES 27/06/2023
h.1518Sala Polifunzionale  Dipartimento di informatica 28/06/2023
h.1013Sala Polifunzionale  Dipartimento di informatica 30/06/2023
h.1114Sala Riunioni Est  Dipartimento di informatica Exam Session III  07/07/2023 TIME PLACE NOTES 17/07/2023
h.1416Sala Polifunzionale  Dipartimento di informatica 18/07/2023
h.10.3013Sala Polifunzionale  Dipartimento di informatica Exam Session IV  06/09/2023 TIME PLACE NOTES 13/09/2023
h.9.3011.30Sala Seminari Est  Dipartimento di informatica 13/09/2023
h.15.0018Sala Seminari Est  Dipartimento di informatica 
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