First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP7079397, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2017/18
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination HUMAN DATA ANALYTICS
Website of the academic structure
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge MICHELE ROSSI ING-INF/03

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/03 Telecommunications 6.0

Mode of delivery (when and how)
Period Second semester
Year 1st Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 26/02/2018
End of activities 01/06/2018

Prerequisites: A basic course on probability theory is recommended. A basic programming course is also advised for a correct comprehension of the material.
Target skills and knowledge: The skills that will be acquired are related to the analysis of biosignals and in general to those signals that are generated by human activity at large. In particular, in the course the students will learn to:

- Deal with quasi-periodic signals, extracting some recurrent structure from them (denoising, segmentation): some key signals that will be studied are the electrocardiogram signal (ECG) and motion data from wearables;
- Extract relevant features from biometric datasets;
- Apply classification (clustering) algorithms on biosignals to classify users or build dictionaries for the compact and accurate representation of the data;
- Use unsupervised learning methods, base on artificial neural networks, to perform vector quantization (dictionary learning);
- Use statistical structures to track correlation in the data such as Bayesian networks and Hidden Markov Models;
- Use supervised neural networks as mapping tools to learn patterns and solve classification problems.
Examination methods: The exam will be a written project where the student will have to solve a selected classification problem for a given dataset. The student will have to work on the code (software), produce and discuss the results.
Assessment criteria: The following evaluation criteria will be taken into account:

- Oral presentation skills;
- Quality of written project: clarity of exposition, mathematical rigor;
- Quality of the obtained results;
- Originality of the adopted techniques;
- Problem complexity.
Course unit contents: Part I – Introduction
- Intro: course outline, grading rules, office hours, etc.
- Applications: health, activity-aware services, security and emergency management, authentication systems, analyzing human dynamics

Part II - Tools
- Vector quantization (VQ):
-- Aims, quality metrics
-- Unsupervised VQ algorithms:
--- Self-Organizing Maps (SOM), Time Adaptive-SOM (TASOM)
--- Gas Neural Networks (GNG)

- Deep Neural Networks (DNN)
-- Neural networks brief: concept, examples, training
-- Convolutional Neural Networks (CNN): structure, training

- Sequential Data Analysis:
-- Hidden Markov Models (HMM):
--- Maximum Likelihood for the HMM
--- Forward-backward algorithm
--- Sum-product algorithm
--- Viterbi algorithm
-- Wald's sequential decision theory (iid case)

Part III – Applications (using the tools of Part II)
- Human Activity Learning
-- Activities & sensors: definitions, classes of activities
-- Features: sequence features, statistical features, spectral features, activity context features
-- Activity recognition: activity segmentation, sliding windows, unsupervised segmentation, performance measures and results

- Biometric Data Processing:
-- Quasi-periodic physiological signals
--- Dictionary learning & compression algorithms
- Inertial signals: identity recognition
- Inertial sensors and cameras: gait tracking & profiling

- Voice Recognition:
-- Deep neural network-HMM hybrid system
-- Architecture, training, performance evaluation
Planned learning activities and teaching methods: The course will be based on frontal lessons that will be given by the Professor in charge. The final project will also serve to put the techniques discussed in the classes into good use, and verify their effectiveness when applied to real signals.
Additional notes about suggested reading: The course material will consist of:

1) book chapters (different and selected books will be used, depending on the specific topic);
2) scientific papers;
3) slides.

All the written material will be in English. The material of the above points 1), 2) and 3) will be made available (password protected) through the course site.
Textbooks (and optional supplementary readings)
  • Bishop, Christopher M., Pattern recognition and machine learningChristopher M. Bishop. New York: Springer, --. Cerca nel catalogo
  • Cook, Diane J.; Krishnan, Narayanan C., Activity learningdiscovering, recognizing and predicting human behavior from sensor dataDiane J. Cook, Narayanan C. Krishnan. Hoboken: NJ, Wiley, 2015. Cerca nel catalogo
  • Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, Machine Learning Refined: Foundations, Algorithms, and Applications. --: Cambridge University Press, 2016.
  • Goodfellow, Ian; Courville, Aaron, Deep learningIan Goodfellow, Yoshua Bengio and Aaron Courville. Cambridge: MA [etc.], MIT Press, 2016. Cerca nel catalogo