The course will provide tools for the analysis of big data (audio, video, text) generated by modern information and communication systems and related services.
Skills stemming from computer science, statistics and optimization will be introduced to provide the means for understanding, designing and implementing methods capable of managing complex amounts of data, and transforming them into useful and semantically relevant information.
Topics to be discussed will include advanced principles of information theory (sparse coding, compressive sensing, random matrix), principles of statistical inference, methodologies for clustering the observed data, predictive analytics, and principles of constrained optimization based on elements of game theory.
Skills stemming from computer science, statistics and optimization will be introduced to provide the means for understanding, designing and implementing methods capable of managing complex amounts of data, and transforming them into useful and semantically relevant information.
Topics to be discussed will include advanced principles of information theory (sparse coding, compressive sensing, random matrix), principles of statistical inference, methodologies for clustering the observed data, predictive analytics, and principles of constrained optimization based on elements of game theory.
teacher profile teaching materials
inference and statistical hypothesis testing
regression
Machine Learning
classification (supervised learning)
decision trees, random forests, naïve Bayes, linear discriminant analysis, k-nearest neighbor, support vector machines
clustering (unsupervised learning)
k-means clustering
hierarchical clustering
data modeling
principal component analysis, indipendent component analysis, outlier detection and data cleansing, hidden Markov models
deep learning & CNN
Processing
parallel processing
examples in Matlab & Python
Data analytics in business applications
Students' presentations
G. James, D. Witten, T. Hastie, R. Tibshirani, "An Introduction to Statistical Learning"
K. P. Murphy, "Machine Learning - A Probabilistic Perspective"
S. Theodoridis and K. Koutroumbas, "Pattern Recognition"
T. A. Runkler, "Data Analytics - Models and Algorithms for Intelligent Data Analysis"
I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning"
Slides from teacher
Programme
Statisticsinference and statistical hypothesis testing
regression
Machine Learning
classification (supervised learning)
decision trees, random forests, naïve Bayes, linear discriminant analysis, k-nearest neighbor, support vector machines
clustering (unsupervised learning)
k-means clustering
hierarchical clustering
data modeling
principal component analysis, indipendent component analysis, outlier detection and data cleansing, hidden Markov models
deep learning & CNN
Processing
parallel processing
examples in Matlab & Python
Data analytics in business applications
Students' presentations
Core Documentation
S. Nolan and T. Heinzen, "Statistics for the Behavioral Sciences"G. James, D. Witten, T. Hastie, R. Tibshirani, "An Introduction to Statistical Learning"
K. P. Murphy, "Machine Learning - A Probabilistic Perspective"
S. Theodoridis and K. Koutroumbas, "Pattern Recognition"
T. A. Runkler, "Data Analytics - Models and Algorithms for Intelligent Data Analysis"
I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning"
Slides from teacher
Type of delivery of the course
Lectures, exercises, presentationsType of evaluation
Oral discussion, presentation on a topic selected by the teacher