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Mathematics for Machine Learning
A matrícula neste curso é apenas por convite

In this online module you can find complementary information and materials that helps to follow the Mathematics for Machine Learning (M4ML) course. The very short pre-lecture preparation online quizzes can be acccessed here.

About This Course

In this online module you can find complementary information and materials that helps to follow the Mathematics for Machine Learning (M4ML) course. The very short pre-lecture preparation online quizzes can be acccessed here.

The main goal of M4ML course is to understand the Mathematics, in particular, Linear Algebra, on which Machine Learning depends. Know how to apply the mathematical concepts in relevant applications as dimension reduction, neural networks and others.

Requirements

It is supposed that you've been previously enrolled in undergraduate courses such as Linear Algebra, Differential and Integral Calculus II, Probability and Statistics. Programming skills are also welcome.

Course Staff

Ana Moura Santos

Ana Moura Santos

Received her diploma in Physics-Mathematics Sciences from the University of Moscow, and the MSc and PhD degrees in Applied Mathematics from Técnico, where she started teaching in 1987, first at the Department of Physics and, from 1993 on, at the department of Mathematics.

The area of her research is Operator Theory and Functional Analysis with applications, and also works on pedagogical issues, namely developing e-learning resources for projects in Mathematics.

She spends most of her free time in activities related to music and dance, presently practicing castañuelas and flamenco.

Pedro Santos

Pedro Santos

Pedro A. Santos is professor at the Math and Computer Science Departments of Instituto Superior Técnico – University of Lisbon. He teaches a wide range of courses from Mathematics to Game Design and Development, and Machine Learning. He is also a Senior Researcher at INESC-ID.

He has a Diploma degree in Applied Mathematics and Computation, a Master’s degree in Applied Mathematics and a doctoral and habilitation degrees in Mathematics. His current research interests and contributions go from Operator Algebras to Game Design, Artificial Intelligence, and its applications.

He is a co-author of a book on Non-Commutative Gelfand Theories (2011), a book on Game Design and Development (2014), a book on Operator Algebras (2020) and is author and co-author of more than 70 peer-reviewed papers published in scientific journals or in conference proceedings.

Rosário Oliveira

M. Rosário Oliveira

M. Rosário Oliveira has a PhD in Mathematics, from the University of Lisbon. In 2023 she defended her Habilitation in Mathematics (Statistics and Stochastic Processes) at the University of Lisbon (ULisbon). She is an Associate Professor at the Dep. Mathematics (DM) from Instituto Superior Técnico (IST), ULisbon since 2020.

In her research activity, she has achieved a balance between theoretical and applied work. She has actively collaborated with specialists from other areas, particularly in Telecommunications and Health Sciences. The applied work has been useful in the identification of theoretical problems that she has addressed in her scientific activity. She has been involved in many projects as an expert in Applied Statistics and Data Science, and she did consult work in these areas.

She has also been involved in many pedagogical activities. Notably, she is the coordinator of the MSc Data Science and Eng. and Minor in Data Science. She has supervised many students from all cycles.

He has a Diploma degree in Applied Mathematics and Computation, a Master’s degree in Applied Mathematics and a doctoral and habilitation degrees in Mathematics. His current research interests and contributions go from Operator Algebras to Game Design, Artificial Intelligence, and its applications.

He is a co-author of a book on Non-Commutative Gelfand Theories (2011), a book on Game Design and Development (2014), a book on Operator Algebras (2020) and is author and co-author of more than 70 peer-reviewed papers published in scientific journals or in conference proceedings.

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