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MACHINE LEARNING

CODE 106788
ACADEMIC YEAR 2021/2022
CREDITS
  • 10 cfu during the 1st year of 8732 INGEGNERIA ELETTRONICA (LM-29) - GENOVA
  • 5 cfu during the 1st year of 8732 INGEGNERIA ELETTRONICA (LM-29) - GENOVA
  • 5 cfu during the 2nd year of 8732 INGEGNERIA ELETTRONICA (LM-29) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/01
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    MODULES This unit is a module of:
    TEACHING MATERIALS AULAWEB

    AIMS AND CONTENT

    AIMS AND LEARNING OUTCOMES

    The aim of the course is to provide the basis for the design and development of classification and regression software algorithms. The student is introduced to different concepts of machine learning (linear models, decision trees, ensemble learning, artificial neural networks, etc.) and supported through extensive exercises during lectures exploiting several software library in Python (NumPy, Pandas, SciKitLearn e TensorFlow). The last part of the course will be focused on the model deployment on embedded systems.

     

    TEACHING METHODS

    The course is composed of a set of frontal lessons and a set of practice sessions. During the frontal lesson, the teacher presents the topics providing also examples of live code that are tested on a Jupyter notebook. Students can use their own laptops during the lecture in order to reproduce what is proposed by the teacher. During the practice sessions, the students have to face up with real problems that they should solve by applying the techniques learnied during the lectures.

    SYLLABUS/CONTENT

    Part 1 - Fundamental algorithms and techniques
    Introduction
    Regression
    Classification
    Linear Models
    Decision trees
    Ensemble Learning
    Dimensionality Reduction
    Unsupervised algorithms

    Part 2 - Neural Networks
    Perceptron
    MLP
    Backpropagation
    Convolutional networks
    Advanced Vision

    Part 3 - Deployment on embedded devices
    Inference
    Quantization
    TensorFlow Lite and TensorFlow Micro
    Deployment on NVidia Jetson platform
    TinyML

    TEACHERS AND EXAM BOARD

    Exam Board

    ALBERTO OLIVERI (President)

    RICCARDO BERTA (President Substitute)

    EDOARDO RAGUSA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The exam is an oral examination on the theoretical topics covered during lectures. In particular, the student has to provide fluency in the description of the main concepts of machine learning.

    ASSESSMENT METHODS

    During the oral exam, the teacher asks the student to illustrate some concepts learned in class. For each concept, the student has to present the definition, the conditions of applicability and pros/cons in relation to other approaches. During the examination, the teacher verifies that the concepts have been learned at a level of knowledge that allows the student to apply them in real cases.