Applied Computer Science
(Machine Learning and Big Data)

Graduates will learn about the scientific, technological and managerial aspects of Machine Learning and Big Data and their various applications. Graduates will go on to work in applied and industrial research and will be able to frame the most advanced and current developments in applied computer science in international research

Graduates acquire methodological knowledge and understanding as follows:

• theoretical and practical aspects of Machine Learning, with particular attention to Deep Learning techniques;
• theoretical and practical aspects of image and video analysis and understanding;
• the fundamentals of classical and quantum physics;
• theoretical and practical aspects of spatial, parallel, distributed and streamed Big Data;
• tools for analysing, visualising, organising and understanding corporate Big Data;
• Machine Learning methodologies and techniques for the analysis and processing of multimedia data;
• theoretical and practical aspects of distributed operating systems and cloud computing for big data.

Students acquire these knowledge and skills during the core courses, elective courses and activities related to the preparation of the final thesis and are assessed through the assessment activities provided for each course and the final exam.

Through extensive and varied laboratory work and collaboration with research laboratories and companies, Master’s graduates acquire the ability to understand, interact with and solve application problems arising from diverse scientific and technological fields. Graduates are able to critically and consciously apply the methodologies and tools of Applied Informatics and to objectively and quantitatively analyse the solutions they propose and develop. Graduates acquire the ability to understand, interact with and solve application problems in various fields. More specifically, graduates will learn to:

• Design and develop programmes of varying levels of complexity using TensorFlow, Torch, Caffe, Python, J2EE and Swift.
• Use computing tools and techniques, such as software development tools.
• Analyse problems related to processing and information technology and identifying solutions to them.
• Give technical presentations. Prepare technical reports.
• Understand the needs of end users and issues related to the design, management and performance of large-scale software.
• Conduct bibliographic research on Machine Learning and Big Data.
• Match problems with the most appropriate tools and techniques to solve them.
• Analyse the computation and processing of related problems and identify solutions for them.
• Develop an understanding and practice of more advanced computing topics, including deep learning, machine learning, audio and video analysis, and the Internet of Things.
• Plan, conduct and write a software development programme to be implemented as a team. Plan, conduct and write an original research and software development programme.

FURTHER INFORMATION

All information on the course, from admission to the final exam, for all students interested in enrolling in the Master’s Degree Course in Applied Computer Science (Machine Learning and Big Data).

Admission requirements

1. To enrol in the Master’s Degree in Applied Computer Science (Machine Learning and Big Data), you must have a three-year degree, or a university diploma, or another recognised qualification obtained abroad, regardless of the location and class of origin. In addition, applicants must have acquired the appropriate curricular requirements and adequate professional preparation in relation to the knowledge required for admission. Specific educational objectives of the programme and description of the educational track The admission procedures are indicated annually in a call for applications issued by the University. Students must first apply for authorisation to enrol through an online procedure. Approval is issued by a committee appointed by the relevant department after analysing the student’s previous university career documentation to verify that the curricular requirements are met and that the student has adequate personal preparation. The curricular requirements are: possession, at the time of application, of the minimum curricular requirements represented by 45 CFU distributed as follows:

1    physics for at least 5 credits;

2. computer science for at least 22 credits;
3. mathematics for at least 15 credits.

The clearance is automatically granted to all graduates in Computer Science (class C-26 or L-31) from Parthenope University or other Italian universities.

Internships and Placements

The Degree Course Study Manifesto includes 6 credits that are recognised for the completion of Internship activities.

Final Examination

The Final Examination consists of the discussion of an applied-experimental thesis developed by the student. The thesis must be original. The thesis must concern one or more applied topics and must involve both methodological and theoretical skills specific to computer science and a set of design, implementation and evaluation activities, also specific to the computer science sector. The thesis is developed under the guidance of a supervisor, chosen from among the lecturers of the degree programme or from among the lecturers of the Department of Science and Technology. Two supervisors are also permitted. Each thesis is assigned a co-supervisor by the President of the degree programme, who monitors the thesis, providing additional support and feedback, and, at the end, produces a report to be evaluated when the degree mark is awarded.