Advanced recognition systems (2020/2021)

Course code
Marco Cristani
Other available courses
Other available courses
    Academic sector
    Language of instruction
    Teaching is organised as follows:
    Activity Credits Period Academic staff Timetable
    Laboratorio 1 I semestre Marco Cristani

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    Teoria 5 I semestre Marco Cristani

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    Learning outcomes

    The course aims to provide: i) advanced techniques of statistical recognition and machine learning, as discriminative and neural classifiers (deep learning); ii) advanced techniques for the programming of professional code for classification in industrial environments; iii) knowledge of classification problems of the industrial world, and techniques usually used for their resolution. At the end of the course the student must demonstrate to be able to: i) understand if a classification problem can be solved without the existing technologies; ii) understand what type of learning algorithm should be used for training a classifier. Furthermore, he / she must demonstrate that he / she has the ability to apply the acquired knowledge: i) identifying what type of classifier or recognizer should be used in response to a given problem; iii) understanding that the machine learning strategy must be implemented according to the number of training data available; iii) understanding the complexity of the problem of recognition in computational terms; iv) being able to write professional software that recognizes real data, possibly modifying it in relation to the problem under examination. This knowledge will allow the student to understand that measures of error and performance must be taken into account given a specific problem under consideration. Furthermore, this knowledge will enable the student to continue his or her studies autonomously in the context of automatic learning or recognition.