Second cycle degree in DATA SCIENCE

Campus: PADOVA

Language: English

Teaching period: Second Semester


Number of ECTS credits allocated: 6

Prerequisites: A basic course on probability theory is recommended. A basic programming course is also advised for a correct comprehension of the material.
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.
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