Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2021/2022
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Machine Learning for Data Science (2021/2022)
Teaching code
4S009069
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
Primo semestre dal Oct 4, 2021 al Jan 28, 2022.
Learning outcomes
The course aims to provide the basic tools for machine learning, together with specific techniques to deal with large amounts of data, such as deep learning. Theory and techniques will be specifically addressed to data science issues with particular emphasis on data analysis.
At the end of the course the student has to show to have acquired the following skills:
● knowledge of the main types of data (e.g. binaries, texts, sounds, etc.)
● understanding and capability to use the basic elements of descriptive statistics, elementary probability, linear algebra with elements of optimization and regularization
● knowledge of basic machine learning techniques (e.g. support vector machines, random forest, etc.)
● knowledge of basic deep learning techniques (e.g. convolutional neural network, long-short memory machines, etc.)
● knowledge of the basics of Natural Language Processing for, for example, sentiment analysis
● knowledge of the basic issues in the context of measurement and Regression measures, e.g., RMSE (Root Mean Square Error), MAE, Rsquared and adjusted Rsquared)
● knowledge of the basic tools in supervised training, e.g., confusion matrix, accuracy, precision, recall, F1, Curve precision-recall, ROC, average precision, CMC NLP: Bleu, Spice
Bibliography
Examination Methods
The exam involves the discussion of a project proposing a solution to an industrial problem.
The student will present his/her work in about 15 minutes (with or without the use of support material such as slides, written report, demo, etc.), followed by a Q&A session.
For the generation of the mark it will be taken into account:
- performance of the developed system (with different metrics depending on the problem);
- theoretical motivation behind the student's design choices;
- ability to clearly and concisely present the key points of the project;
- ability to support a discussion on possible alternative solutions and potential causes of failure of the solution developed.
The student must also demonstrate mastery of all the topics in the program (even those not addressed during the project).
