Introduction to Machine Learning

Scuola Normale Superiore
LEVEL
Master
TYPE
Course
MODES
-
LANGUAGE
-
ECTS
60
PERIOD
20/04/2022 to 30/06/2022

Course Description

Module 1 (20 hours) Foundation of Data Mining and machine Learning (Prof. Giannotti) – 1) Introduction: the Knowledge Discovery process. All steps in a nutshell- 2) Data understanding and Data exploration- 3) Unsupervised learning: Clustering: intro clustering: K-Means clustering- 4) Unsupervised learning: Clustering: DBSCAN e Hierarchical clustering- 5) practicals: case study on simple data sets iris e titanic (python or Knime)- 6) Supervised Learning: Classification introduction, performance evaluation, a first simple classifier: KNN – 7 ) Supervised Learning: classification with Decision tree- 8) practical: case study on simple data sets iris e titanic- 9) Supervised Learning:Classification advanced: RF, SVM, NN – 10) Unsupervised learning: Pattern mining: a-priori pattern mining: case study Module 2 (20hrs) Application of Machine Learning algorithms in Bioinformatics and Life Sciences (Dr. Raimondi)- 1) protein structure/function prediction using machine learning- 2) protein structure prediction using deep learning and co-evolution- 3) protein language models- 4) application of graph neural network for the prediction of protein interaction networks- 5) deep learning applications to genomics :DNA motif discovery- 6) deep learning applications to genomics: variant intepretation- 7) deep learning applications to genomics: single cell RNAseq analysis and interpretation- 8) practicals: protein model languages- 9) practicals: deep learning for genomics- 10) practicals: multiomics data integration

Subject area

Biology
Digital communications IA electronics

Field area

Health

Educational-info

Competences

Aim of the course is to provide students with basic knowledge of both theoretical foundations and practical aspects of machine learning, with a particular focus on applications to bioinformatics and biology.

Prerequisites

at least a Bachelor

Duration

40h

ECTS

60

Validation mode

Oral Examination

Maximum number of students

10

Organizer

Partner

Scuola Normale Superiore

Faculty

Scuola Normale Superiore

Department

Classe di Scienze

Contact or registration links