ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
Academic Year 2021/2022  2° Year ADVANCED MACHINE LEARNING: Vincenza Carchiolo
 Knowledge Discovery: Concetto Spampinato
Taught classes: 80 hours
Term / Semester: 1° and 2°
Learning Objectives
 ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
 Knowledge Discovery
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of realworld applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
 to understand and use the main methodologies and techniques for learning from data
 to understand the main methodologies to design and implement neural networks for realworld applications
 to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
 to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
 To understand the main concepts of learning from data
 To understand concepts and tools for building intelligent systems using supervision and no supervision
 To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
 To understand what are the most appropriate techniques to be used in different realworld applications
Applying knowledge and understanding
 To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
 To design and implement from scratch a machine learning system following applicationderived constraints in terms of modelling and data
 To understand proper benchmarks and baselines and analysing achieved results and their generalization in realworld applications
 To be able to apply methodologies and techniques to analyse data.
Course Structure
 ADVANCED MACHINE LEARNING
Lectures, handson exercises, paper reading, student presentations and seminars
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
 Knowledge Discovery
The main teaching methods are as follows:
 Lectures, to provide theoretical and methodological knowledge of the subject;
 Handson exercises, to provide “problem solving” skills and to apply design methodology;
 Laboratories, to learn and test the usage of related tools.
 Paper reading and presentations to enhance understanding of the core concepts
 Seminars by renowned experts (from both universities and industries) in the field to understand the current state of the art.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Required Prerequisites
 ADVANCED MACHINE LEARNING
Python programming language, Linear Algebra
 Knowledge Discovery
Python programming language, statistical learning basic concepts
Attendance of Lessons
 ADVANCED MACHINE LEARNING
Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.
 Knowledge Discovery
Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the final exam.
Detailed Course Content
 ADVANCED MACHINE LEARNING
Introduction to the Course

Introduction to Machine Learning

Review of data Characteristics of Data and Preparation and Preprocessing
Supervised Learning

Classification and Prediction using KNearestNeighbor

Classifying with Probability Theory; Naïve Bayes

Building Decision Trees

Regression models

Evaluating predictive models

Ensemble Models: Bagging and Boosting
Unsupervised Learning

Clustering using KMeans

Hierarchical Clustering

Association Rule discovery

Principal Component Analysis and Dimensionality Reduction

Singular Value Decomposition
Brief note on Advance Topics

Matrix Factorization

Support Vector Machines

Search and Optimization Techniques

Markov models; time series analysis, sequential pattern mining
Real application domains

Text Mining and document analysis/filtering

Content analysis, TFxIDF transformation, text categorization, document clustering


Recommender systems

Neighborhood methods (user and itembased)

Matrix factorization

Marketing and finance data analysis

 Laboraroty activity

Brief Python review

The package Numpy, Pandas , matplotlib and seaborn

Scikitlearn: a machine learning library for Python

Classification, Regression, Clustering, Dimensionality Reduction, Model Selection, Preprocessing



 Knowledge Discovery
The KD module consists of two parts: the first one will be addressing the general and modern techniques based on deep learning paradigm to create KD systems from data, while the second one on how to extract, represent and visualize knowledge from data and trained models.
Part I: Methods and Architectures
Neural Networks and Backpropagation
 Derivatives and Gradient Descent
 Neural Network Representation, Gradient descent for Neural Networks
 Forward and Back Propagation
 The revolution of depth: deep learning
 Optimization algorithms: Minibatch gradient descent, Exponentially weighted average, Gradient descent with momentum, RMSprop, Adam optimization algorithm, Learning rate decay
 Training aspects of deep learning: Regularization, Dropout, Normalizing inputs, Vanishing / Exploding gradients, Weight Initialization for Deep Networks
Convolutional Neural Networks
 Foundations: padding, strided convolution, dilation, 2D and 3D convolution, pooling
 State of the art models: AlexNet, ResNets, DenseNets, Inception
 Transfer Learning and Data Augmentation
Recurrent Neural Networks
 LSTM and variants
 Attention mechanisms
Part II: Knowledge Discovery from Data and Models
Unsupervised Learning with Deep Networks
 Representation and Feature Learning
 Autoencoders and Variational Autoencoders
 Generative Adversarial Networks
Reinforcement Learning
 Introduction to Reinforcement Learning
 Policy Gradients
 ActorCritic Algorithms
 Value Function Methods
 Deep RL with Qfunctions
Deep Learning Frameworks:
 Overview of the most used DL frameworks
 PyTorch and Jupyter Notebooks
Applications:
 Computer vision
 Medical Image Analysis
 Machine translation
Textbook Information
 ADVANCED MACHINE LEARNING

Introduction to Machine Learning, Fourth Edition, By Ethem Alpaydin, MitPress ISBN: 9780262043793. 2020

Python Data Science Essentials  Third Edition by Alberto Boschetti, Luca Massaron, Packt Publishing, ISBN: 9781789537864, 2020

Teaching materials and reading paper list provided by the instructor

 Knowledge Discovery

Deep Learning. I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016

Programming PyTorch for Deep Learning, I. Pointer, O'Reilly Media

Teaching materials and reading paper list provided by the instructor

Course Planning
ADVANCED MACHINE LEARNING  
Subjects  Text References  

1  Introduction to Machine Learning  1,3  
2  Python review  3  
3  Pandas, Numpy, Matplotlib  2,3  
4  Classification and Prediction: KNearestNeighbor  1  
5  Classification and Naive Bayes  1  
6  Decision Tree  1,3  
7  Regression models  1  
8  Evaluating predictive models  1  
9  Ensemble Models: Bagging and Boosting  1  
10  Clustering using KMeans  1  
11  Hierarchical Clustering  1  
12  Association Rule discovery  1  
13  Dimensional reduction  1  
14  Singular Value Decomposition  1  
15  Advanced topic  1,3  
16  Sckit learn  3  
17  ML in NLP  3  
18  ML for Reccomender System  3  
Knowledge Discovery  
Subjects  Text References  
1  Neural networks: derivatives, gradient descent, backpropagation  3  
2  Deep Learning: basic concepts, optimization algorithms, training procedures  1, 3  
3  Convolutional Neural Networks  1,3  
4  Recurrent Neural Networks  1,3  
5  Unsupervised Learning with Deep Networks: Representation and Feature Learning  1, 3  
6  Autoencoders and Variational Autoencoders  1, 3  
7  Generative Adversarial Networks  1, 3  
8  Reinforcement Learning: Deep QNetworks and Policy Gradient  3  
9  Deep Learning Frameworks: PyTorch and Jupyter Notebooks  2, 3 
Learning Assessment
Learning Assessment Procedures
 ADVANCED MACHINE LEARNING
There will be one assignment and one final exam. The assignments will contain written questions that require some Python programming. The final exam consists a final assignment and an oral discussion concerning all course material.
The final assignment concerns comparative analysis on a given problem that must be presented in a final report and discussed in an oral discussion. The vote on the advanced machine learning module will account for 40% of the total grade for the entire course.
The grading policy for the AML module is:

40%: Final assignments

20% Intermediate assignments

40%: Oral discussion
Learning assessment may also be carried out on line, should the conditions require it.

 Knowledge Discovery
The final exam consists of the development of a project in Pytorch, addressing one of the topics discussed during classes, together with a final report (structured as a scientific paper) discussing motivation, models, datasets and results used in the project.
The exam is evaluated according to the ability to create a deep learning model from scratch for extracting and learning knowledge from data on a given realworld problem, to understand how to properly measure its performance and to motivate the devised solutions.
The vote on the knowledge discovery module will account for 50% of the total grade for the entire course.
The module also foresees intermediate assignments. These assignments (between two to four) include: a) python scripts to solve simple basic learning problems on datasets discussed during with the instructor in order to avoid overlap and b) quizzes to verify the correct understanding of the presented techniques.
The grading policy for the KD module is:

50%: Final project

35%: Programming assignments

15%: Quizzes
Learning assessment may also be carried out on line, should the conditions require it.

Examples of frequently asked questions and / or exercises
 ADVANCED MACHINE LEARNING
Examples of questions and exercises are available on the Studium platform and on the course website.
 Knowledge Discovery
Examples of questions and exercises are available on the Studium platform and on the course website.