The Second-level Short Specialisation Degree in Machine Learning and Big Data in Precision Medicine and Biomedical Research prepares biomedical professionals with techniques that can be implemented in data processing, in order to make the most of the information potential of big data.
In fact, the course is oriented towards learning how to use advanced methods of analysing such data and is very much focused on being hands-on and practical applications.
In terms of machine-learning techniques in the biomedical field, a case study will be presented for each machine learning technique, involving the use of R software and programming language with training by expert lecturers.
The Second-level Short Specialisation Degree in Machine Learning and Big Data in Precision Medicine and Biomedical Research trains students in data processing using modern machine learning techniques. The course explores the application know-how that is of increasing importance to all aspects of medical practice and research. From machine learning to data mining to predictive analytics in the clinical field, the Master’s delves into the techniques that are the methodological tools at the basis of personalised medicine.
The course has various modules as explained below.
The Second-level Short Specialisation Degree in Machine Learning and Big Data in Precision Medicine and Biomedical Research is mainly aimed at professionals already working or intending to work in the biomedical field (especially biostatisticians, bioengineers, bioinformaticians, and computational biologists).
In terms of job opportunities, the application of machine learning techniques in the medical field is expected to change the way we think about diagnosis and treatment, with a focus on personalised medicine. Therefore, the knowledge obtained in this Master’s course will allow current healthcare professionals to be more specialised and will also allow for defining the types of technical/quantitative professionals that can work in the healthcare sector, in biomedical research, and in CROs.
The Second-level Short Specialisation Degree in Machine Learning and Big Data in Precision Medicine and Biomedical Research will provide training in:
Module 1 – MACHINE LEARNING FOR PREDICTING OUTCOMES
The ability to predict health outcomes is a fundamental aspect of medical and clinical practice. Prediction covers many application areas ranging from prognosis to therapy evaluation. This course also discusses supervised MLTs for numerical prediction and classification.
Module 2 – MACHINE LEARNING FOR PREDICTING CLINICAL CONDITION
This module introduces basic and advanced techniques used in clinical and biomedical research in prediction and stratification problems, with a focus on the identification of the clinical condition of patients, their response to different therapeutic strategies and how they pertain to specific stages of progression. The module covers topics related to supervised and unsupervised learning, such as GLMs, GMMs, Bayesian networks, and survival models. Starting with the fundamentals of the various methodologies, we consider how these advanced techniques can be applied to biomedical research and clinical practice scenarios.
Module 3 – MANAGEMENT OF HETEROGENEITY IN BIOMEDICAL RESEARCH AND CLINICAL PRACTICE
In this module, standard and advanced clustering techniques are introduced, such as k-means hierarchical clustering, and self-organising maps. The course presents examples of applications in terms of biological and clinical data.
Module 4 – ADVANCED TECHNIQUES FOR PREDICTION AND STRATIFICATION
This module introduces advanced techniques for prediction and stratification in biomedical research and clinical practice, such as neural networks, support vector machines, and non-negative matrix factorization. Starting with the fundamentals of the various methodologies, we consider how these advanced techniques can be applied to biomedical research and clinical practice scenarios.
Module 5 – DEEP LEARNING IN CLINICAL SETTINGS FOR BIOMEDICAL RESEARCH
Deep learning (DL) is introduced in this module. The module covers the following topics:
- theoretical foundations of this innovative tool
- most popular programming language for DL: Python
- application of DL for testing and image analysis with a focus on medical datasets
- training processes
- how DL can be applied to small datasets
- tools and libraries for the development of DL systems
The final assessment of the Master’s course will be the average of the grades from the homework completed during the year (70% of the final assessment) along with the grade from the final project work (30% of the final assessment).
To learn about Directors and Lecturers and get other useful information about the Second-level Short Specialisation Degree in Machine Learning and Big Data in Precision Medicine and Biomedical Research, you can view the video presentation: Machine Learning and Big Data in Precision Medicine and Research Machine Learning and Big Data – UBEP (unipd-ubep.it).
The general ranking of merit for the academic year 2023/24 will be published on the Italian page of this Master according to the timing provided in the Call.
The Second-level Short Specialisation Degree is a second-level course, lasts one year, is online and can also be attended by people working full-time because it is available on demand. It is designed for those who need further qualifications or specialisation but who are busy with other professional pursuits. There is frequent, easy interaction between students and lecturers through the Moodle forum of the University of Padua.
The modules – approximately four weeks each – are followed by a one-week break, with video lessons between November and May. At the end of each module, homework is assigned to assess the skills acquired. The homework is also available asynchronously at the end of each module and must be handed in within three weeks of the end of that module. At the end of the lessons, project work is assigned, which can also be defined (in agreement with one’s advisor) on the basis of the student’s personal and professional interests. The work, to be completed over the summer between June and July, will be the basis for the diploma discussion, which will take place in September, over Zoom.
No. There are no internships or apprenticeships planned because they potentially may not be compatible with the commitments the students in this Master’s course already have. However, students will have the opportunity to take on scientific issues and deal with real databases during lectures and project work. Students may work with lecturers or their advisor in order to carry out the project work in an area of specific personal and professional interest.
Attendance is compulsory, even if lessons are held online. The maximum absence rate is 30%. However, since the video lectures are pre-recorded and can be consulted 24/7, it is very easy to keep up with studies. The educational coordinator and lecturers are on hand to try to accommodate students if their schedules are busy or if they have especially intense workloads.
No, there are no scholarships offered.