HUMAN DATA ANALYTICS
Corso
A Padova
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Descrizione
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Tipologia
Corso
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Luogo
Padova
This is a course on advance and applied machine learning techniques, that are applied to real world problem within the human data domain. Given this, the examination of the student will be carried out through a project which will involve the following phases of work:
1. The instructor will identify a problem to solve, using an open, rich, and freely accessible data set. The problem to tackle will be thus described by the instructor during a specific lesson where he will as well present how to carry out the final exam, which will consist of: 1) delivering a written report and 2) giving a conference-style talk
2. The students will split into groups, with a maximum of two students per group, and will start to work to the assigned project. The choice of the specific technique to use, the data pre-processing algorithm to obtain informative features, etc., will all be identified in full autonomy by the students, as a first step. The instructor will be available to steer the work and follow the students along all the work phases
3. Each group will solve the assigned problem using the selected technique and will: 1) present a final written report, 2) give a conference-style talk describing: the problem, the selected models / techniques, the software written as part of the project development, the obtained results. It is also recommended that the students will showcase their software during the presentation
A final grade will be provided by the instructor upon a close inspection of the written report at point 1) and the assessment of the talk at point 2).
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Opinioni
Materie
- E-learning
- Python
Programma
- Intro: course outline, graduation rules, office hours, etc.
- Applications: health, activity-aware services, security and emergency management, authentication systems, analyzing human dynamics
Part II – Vector Quantization (12 hours)
- Vector quantization (VQ):
--- Aims, quality metrics
--- K-means, soft K-means, Expectation Maximization
- Unsupervised VQ algorithms:
--- Self-Organizing Maps (SOM), Gas Neural Networks (GNG)
- Application to quasi-periodic biometric signals (ECG):
-- Signal pre-processing, normalization, segmentation
--- Dictionary learning: concepts, architectures
--- Efficient representation of ECG signals: description of state-of-the-art algorithms
--- Unsupervised dictionary designs for ECG via GNG-based dictionaries
--- Final system design and numerical results
Part II – Sequential data analysis (10 hours)
- Hidden Markov Models (HMM):
--- Maximum Likelihood for the HMM
--- Forward-backward algorithm
--- Sum-product algorithm, Viterbi algorithm
- Applications
--- Authentication: user identification from keyboard keystroke dynamics
--- Speech recognition: audio feature extraction, automatic speech recognition through HMM
Part III - Deep Neural Networks (10 hours)
- Gradient descent and general concepts (supervised learning, overfitting, cost models, etc.)
- Feed Forward Neural Networks: models, training, back-propagation
- Convolutional Neural Networks (CNN): structure, description of constituting blocks, training
- Applications: 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
- User authentication from motion signals: combination of CNN-SVM and sequential estimation theory
- Object / face recognition through CNN
Part IV: Laboratory classes (12 hours)
In the laboratory classes the students will go through a guided tour through the construction of Python code for neural networks, writing all the building blocks related to: the creation of the neural network structure, its training using several gradient descent-based algorithms. The students will be exposed to Python programming, including the use of the Keras and TensorFlow frameworks for the implementation and training of neural network structures. The software composing the different blocks of the presented neural network architectures will be pre-written and checked for correctness, so that the students, after attempting to implement their own version of it, will succeed to combine the various blocks and complete the assigned task. Upon connecting the blocks into the selected neural network architecture, the obtained neural network models will be trained using several gradient descent algorithms, and tested against selected and real datasets. The topics that will be covered are:
- Introduction to Python programming
- Solving a baseline inference problem
- Feed forward neural networks
- Convolutional neural networks
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HUMAN DATA ANALYTICS