Alfredo Troiano | COMPUTER SCIENCE FOR INDUSTRY 4.0: NETWORKING, BIG DATA MANAGEMENT AND MACHINE LEARNING
Alfredo Troiano COMPUTER SCIENCE FOR INDUSTRY 4.0: NETWORKING, BIG DATA MANAGEMENT AND MACHINE LEARNING
cod. 0623000004
COMPUTER SCIENCE FOR INDUSTRY 4.0: NETWORKING, BIG DATA MANAGEMENT AND MACHINE LEARNING
| 0623000004 | |
| DEPARTMENT OF INDUSTRIAL ENGINEERING | |
| EQF7 | |
| SMART INDUSTRY ENGINEERING | |
| 2023/2024 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2021 | |
| FULL ACADEMIC YEAR |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 12 | 120 | LESSONS |
| Objectives | |
|---|---|
| the course aims to introduce the main it technologies and methodologies underlying the functional paradigm of industry 4.0. in particular, it will first introduce the principles that have guided and are guiding the development of computer networks. through the analysis of the tcp/ip protocol stack, it will be possible to introduce its main functional characteristics and describe network protocols and topologies with a focus on mesh networks. in addition, the course will introduce basic concepts related to cyber security. a review of the main methodological and practical approaches related to distributed programming and its functional paradigms will close this section of the module. thanks to the internet and its protocols, the pervasiveness index of computing tools has grown significantly, giving rise to the functional paradigm of the internet of things. this increase has led to the production of a huge amount of data so much so that it is called big data. big data is one of the most characteristic aspects of the industry 4.0 world, and the second part of the course will address the problems associated with its management and exploitation. the limitations of traditional technologies in contexts where volume, throughput, or type make their management challenging will be discussed. architectures, techniques, and tools for managing and processing this large amount of data will be presented. finally, in its third part, the course will also provide the main theoretical concepts of machine learning and the appropriate techniques and tools to apply these approaches in an industrial context. a final project work is planned in which through the use of the python language and the most widely used open-source machine learning software packages on the market, problems present in real-world contexts will be addressed. |
| Prerequisites | |
|---|---|
| no prerequisites |
| Contents | |
|---|---|
| introduction to computer networks the internet and its protocols the tcp/ip protocol stack - application layer - transport layer - internet layer - data link layer - physical layer cybersecurity introduction to cybersecurity internet of things big data databases for big data cloud and big data introduction to big data analysis and management the python language machine learning and artificial intelligence deep learning case studies data mining lab with open-source environments |
| Teaching Methods | |
|---|---|
| the course includes - face-to-face lectures for a total of 60 hours - classroom-assisted exercises: 20 hours. students will develop, autonomously or in small groups, summary exercises on theoretical topics introduced previously. - laboratory activities: 40 hours. students will be required to develop, through a problem-solving-based approach, project works related to the main topics developed in the theoretical part. |
| Verification of learning | |
|---|---|
| the achievement of the educational objectives is certified by an examination with a rating in the thirtieths (the minimum passing level is "18" and the maximum is "30 cum laude"), which includes an oral test that includes the discussion of a project work, previously developed, lasting approximately one hour. during the examination, the committee - checks the level of knowledge of the topics covered in the theoretical lectures; - verifies the ability to present on a given topic; - verifies the autonomy of judgment in proposing the most appropriate approach to argue what is required. - ascertains the student's ability to identify strategies for solving complex problems. specifically, the oral test consists of three questions: the first one is about the project work and its solution strategy, and the second and third questions will cover the topics covered during the lectures (e.g., the following topics. main network protocols, distributed architectures, distributed programming examples, main features of big data, big data analytics, machine learning techniques). the student achieves the level of excellence if he or she demonstrates the ability to make connections between the theoretical topics covered and demonstrates full mastery of the activities performed to develop the written test. |
| Texts | |
|---|---|
| ames f. kurose, keith w. ross, computer networks and the internet. a top-down approach, pearson thomas erl, wajid khattak, paul buhler, big data fundamentals: concepts, drivers & techniques, pearson paul j. deitel, harvey deitel, intro to python for computer science and data science: learning to program with ai, big data and the cloud, pearson joel grus, data science con python, egea-o’really andre de mauro, big data analytics, apogeo |
| More Information | |
|---|---|
| The lectures will be delivered in English |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-12-17]
