In this course, the students are introduced to the basic mathematical concepts required to understand deep learning. They start from the general ideas of applied mathematics that allow to define multivariable functions, find the maximum and minimum points of these functions as well as to quantify the degrees of certainty. Next, the fundamental goals of machine learning are analyzed. The students examine how to achieve these goals by defining a model which has specific targets, designing a cost function which measures how well those targets correspond to reality whilst using a training algorithm in order to minimize this cost function. This fundamental framework is the basis for a wide range of machine learning algorithms, including non-deep learning approaches. However, the main aim is to develop deep learning algorithms within the defined framework.

• Linear Algebra
• Probability and information theory
• Numerical Calculations
• Introduction to pattern recognition and machine learning 
• Statistical data analysis

• Algorithms
• Image recognition using matrices
• Quaternions  or Hamiltonian Quaternions (use case: animation)