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PORTALE DELLA DIDATTICA

Machine learning for pattern recognition

01SCSIU

A.A. 2020/21

Course Language

English

Course degree

Doctorate Research in Ingegneria Informatica E Dei Sistemi - Torino

Course structure
Teaching Hours
Lezioni 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Cumani Sandro   Ricercatore a tempo det. L.240/10 art.24-B ING-INF/05 20 0 0 0 2
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
PERIOD: APRIL - MAY This course gives a broad yet rigorous introduction to machine learning and statistical pattern recognition. It focuses on supervised generative and discriminative learning models, analyzing some important topics such as model architectures, training and evaluation techniques. The course will compare different models and classification approaches on the popular MNIST digit recognition dataset. It will also discuss about the applications of the proposed machine learning approaches to image, speech, and speaker recognition. Students will be asked to apply the acquired knowledge to develop their own classification system using labeled training and evaluation data provided during the course. Each system will then be evaluated on another unlabeled and previously unseen data set. This course belongs to an educational path on Data Science. The path is composed by - an introductory course (Data Mining: Concepts and Algorithms), covering data analytics fundamentals, which is a cultural prerequisite for the other courses - 5 thematic courses dealing in depth with specific Data Science topics, such as different algorithm types or application domains: - Data Analytics for Science and Society - Machine Learning for Pattern Recognition - Mimetic Learning - Text Mining and Analytics - Visualization and Visual Analytics
PERIOD: APRIL - MAY This course gives a broad yet rigorous introduction to machine learning and statistical pattern recognition. It focuses on supervised generative and discriminative learning models, analyzing some important topics such as model architectures, training and evaluation techniques. The course will compare different models and classification approaches on the popular MNIST digit recognition dataset. It will also discuss about the applications of the proposed machine learning approaches to image, speech, and speaker recognition. Students will be asked to apply the acquired knowledge to develop their own classification system using labeled training and evaluation data provided during the course. Each system will then be evaluated on another unlabeled and previously unseen data set. This course belongs to an educational path on Data Science. The path is composed by - an introductory course (Data Mining: Concepts and Algorithms), covering data analytics fundamentals, which is a cultural prerequisite for the other courses - 5 thematic courses dealing in depth with specific Data Science topics, such as different algorithm types or application domains: - Data Analytics for Science and Society - Machine Learning for Pattern Recognition - Mimetic Learning - Text Mining and Analytics - Visualization and Visual Analytics
- Basic knowledge of probability and statistics, linear algebra and calculus
- Basic knowledge of probability and statistics, linear algebra and calculus
- Introduction to pattern classification - Decision theory - Classification model taxonomy - Generative probabilistic models - Discriminative probabilistic models - Decision functions - Model Evaluation - Binary classification - Accuracy measures - ROC / DET curves - Multiclass classification - Generative probabilistic models - Gaussian models - Gaussian mixture models - Expectation-Maximization for GMMs - Latent variable models: Factor Analysis - Introduction to Factor Analysis - Linear-Gaussian models - Probabilistic Principal Component Analysis - Probabilistic Linear Discriminant Analysis - Subspace models for GMMs
- Introduction to pattern classification - Decision theory - Classification model taxonomy - Generative probabilistic models - Discriminative probabilistic models - Decision functions - Model Evaluation - Binary classification - Accuracy measures - ROC / DET curves - Multiclass classification - Generative probabilistic models - Gaussian models - Gaussian mixture models - Expectation-Maximization for GMMs - Latent variable models: Factor Analysis - Introduction to Factor Analysis - Linear-Gaussian models - Probabilistic Principal Component Analysis - Probabilistic Linear Discriminant Analysis - Subspace models for GMMs
A distanza in modalitÓ sincrona
On line synchronous mode
Presentazione report scritto
Written report presentation
P.D.2-2 - Aprile
P.D.2-2 - April


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