Machine Learning Course

ML is a set of methods that in some sense represents the future of advanced analytics, providing fast, accurate and flexible solutions to data analysis applicable in different fields such as genomics, chemistry, neuroscience, economics, finance and social sciences. It allows to obtain deep insights of data, recognize unknown patterns and create high performing predictive models.
There is growing interest on ML both in academic and industrial environment, thus increasing the need of professionals with such skills.
This course intends to provide a theoretical background and advanced topics on Machine Learning and its application in Genomics, Chemistry and Neuroscience with discussions and practical examples in each specific field.
In the fields of Chemistry and biology, application of data science approaches and ML  methods may dramatically enhance our ability to design molecules that can tune the functions of biological systems, with implications for the development of new drugs, or novel materials for energy applications. Common goals are increasing the predictive power and the interpretability of models to provide a better understanding of the correlations between structure and properties of biological and inorganic materials, or to exploit very large genomic data sets to predict diseases or phenotypes in general.
In the field of Genomics, ML allows to address the challenge of detecting the contribution of noncoding mutations to disease. A deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants can be successfully used. This predictive genomics framework has illuminated the role of noncoding mutations in Autistic Spectrum Disorder and has allowed to prioritize mutations with high impact for further study. This is just one of the  possible applications of deep learning in genomics.
Over the last several years, the use of machine learning in Neuroscience has been rapidly increasing. With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is shifting away from the amount and type of available data towards what we actually can do with them. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.