Machine learning for biological structures and networks (2022/2023)
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
The teaching is organized as follows:
The course is aimed at providing the theoretical and applicative basis of Pattern Recognition techniques for the computational analysis of biological objects with a complex structure (such as graphs, sequences, networks, strings and so on). In particular, the course introduces and discusses the most important computational techniques for the analysis of structured data, with particular emphasis on the representation and on the generative and discriminative approaches. Knowledge and understanding: At the end of the course, the student has to demonstrate to be able to apply to real data the methodologies for recognition of complex data, by developing a Pattern Recognition system. Applying knowledge and understanding: a) Representation of biological data with complex structure b) Classification of biological data with complex structure c) Clustering of biological data with complex structure Making judgements: At the end of the course, the student should demonstrate to be able to propose in an autonomous way efficient solutions for a given biomedical and bioinformatics domain, being able to identify critical issues linked to complex bioinformatics problems. Communication: At the end of the course, the tudent should demonstrate to be able to interact with colleagues in work groups. Lifelong learning skills: At the end of the course, the student should demonstrate to be able to learn and autonomously apply novel methodologies for facing bioinformatics and clinical problems. In particular, the student should demonstrate to be able to analyse a biological problem, involving complex and structured biological data, from a Pattern Recognition perspective; he will also have the skills needed to study, invent, develop and implement the different components of a Pattern Recognition System for biological structured data. The student will also be able to autonomously proceed with further Pattern Recognition studies.
Prerequisites and basic notions
Theory: basic notions on Pattern Recognition (a brief recap will be given at the beginning of the course), Basic notions of Algorithms, Probability, Statistics, Algebra.
Lab: Programming skills, Programming language used: Matlab (there will be an introductory lecture for students who are not familiar with Matlab)
CHAPTER 1 Basic Pattern Recognition concepts and introduction to structured data
CHAPTER 2. Representation of structured data
- The Bag of words representation
- The dissimilarity-based representation
CHAPTER 3. Models for structured data
- Generative models
- Bayes Networks
- Learning and inference
CHAPTER 4. Kernels for structured data
- Support Vector Machines e kernel
- Kernels for structured data
CHAPTER 5. Advances Learning paradigms
The course also contains a lab part, where algorithms seen during the theory part will be implemented and deeply analysed
Learning assessment procedures
The exam is aimed at the verification of the following skills:
- capability of clearly and concisely describe the different components of a Pattern Recognition System for structured data
- capability of analise, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem which involves structured data
The exam consists of two parts
i) a written exam containing questions on topics presented during the course (15 points available). The written part is passed is the grade is greater or equal to 9.
ii) an oral presentation of a scientific paper published in relevant bioinformatics journals or conferences on a given argument (decided during the course). The paper is chosen by the candidate and approved by the instructor (15 points available).
The two parts of the exam can be passed separately: the final grade is the sum of the two grades.
The total exam is passed if the final grade is greater or equal to 18. Each evaluation is maintained valid for the whole academic year.