Subjects

  • Introduction to Engineering  
    • This subject aims to develop technical and conceptual skills to analyze and simulate circuits with passive elements, basic communication systems and control systems; as well as solving problems, at a lab-scale, through the design of digital circuits and programmable electronic circuits.


  • Software Engineering  
    • This subject is focused on providing students with an enough overview of theoretical and practical aspects of Software Engineering such as: software process, project planning, requirements engineering, design strategies, informal/formal specification, analysis techniques, model-driven development, testing techniques, software product lines, prototyping and presentation.


  • Algorithms  
    • This subject provides students with the initial concepts and tools for developing an algorithmic thinking as well as basics on programming.


  • Machine Learning  
    • This subject is an introductory course on theoretical and practical concepts to start with machine learning.


  • Mathematical concepts and methods  
    • This course encompasses some basics on algebra and functions as well as introductory concepts on limits.


  • Functions and methods for differential and integral calculus  
    • It is an introductory course on differential and integral calculus.


  • Mathematical theorems  
    • This subject encompasses topics for formulating and demonstrating theorems such as: logic, set theory and definition of operators.


  • Research methods and techniques  
    • This course is aimed at providing students with concepts and background required for developing research processes.


  • Research project  
    • This seminar is for providing students with tools and guidance to develop a research degree thesis.


  • Applied maths  
    • It is an advanced and applied maths course for engineering students, which encompasses topics such as differential equations, complex variable, and Fourier analysis.


  • Digital systems  
    • This subject is an introductory course to digital electronics encompassing topics such as logic gates, combinational logic and sequential logic.


  • Signal analysis  
    • This course encompasses some basics on signal processing such as Fourier analysis, basic signal treatment and singular functions.


  • Biomedical Signal Processing  
    • This subject encompasses topics related to the design of an diagnosis automatic system based on biomedical signal analysis.


  • Differential calculus  
    • The primary objects of study of this course are limits and derivative of functions of one variable.


  • Electromedical Science I, II  
    • The courses Electromedical Science I and II are aimed at providing students with tools and building blocks for analyzing and designing electromedical devices as well as biomedical signal analysis systems.


  • Research Seminar I
    • This subject is aimed to provide students from Faculty of Medicine with basics and constructive blocks to state a problem and generate a first version of a research proposal. As well, this course encompasses concepts and definitions around epistemology and science. Also, political and administrative issues within Colombian context.


  • Methods of study and communication
    • In this subject, the following topics are covered: Study techniques, essay and project writing, Spanish orthography and grammar.


  • Data processing
    • This subject is aimed to provide students from Faculty of Engineering with basics and constructive blocks to solve simple pattern recognition problems.

  • Advanced topics on data processing
    • This subject is aimed to provide students from Faculty of Engineering with tools and constructive blocks to solve real pattern recognition problems.

  • Machine learning
    • This subject is an introductory course on theoretical and practical concepts to start with machine learning.


    Courses
  • Elements of linear algebra and algorithmics for data analytics
    • This course is aimed at training participants in conceptual and theoretical elements for addressing machine learning. Particularly, the area of pattern recognition studied from a linear algebra and functional analysis point of view. In addition, some recommendations are given for the typesetting and presentation of scientific articles in this area.

  • Machine Learning from a matrix algebra viewpoint using MatLab
    • This course is aiming at provinding students with an enough background to start with machine learning within a matrix algebra framework. Simulations are to be conducted using MatLab as well as some matrix equation typesetting basics in LaTeX will be explained.

  • Introduction to image segmentation
    • The aim of this course is the general concepts of acquisition and digitalization of an image, representation, operations and transformations will be addressed, and a basic example of segmentation using non-supervised machine learning techniques using MatLab will be developed.

  • Writing a scientific paper
    • The aim of this course is to provide students with hints and important aspects on how to write an Engineering scientific paper.


  • Academic writing in English
    • The aim of this course is to provide students with basics and tips for academic writing in English.


  • Basic mathematics
    • This course encompasses basics on mathematics: Linear and Quadratic functions, analytic geometry, limits and derivatives.


  • Introduction to matrix algebra for pattern recognition
    • This course encompasses basics on vector spaces and matrix notation, as well as introductory topics on matrix algebra. The aim of this course is to provide students with mathematical background for encouraging learnability of pattern recognition using multivariate structures.


  • Introduction to matrix notation for pattern recognition
    • It is a introductory course on matrix notation widely used in the Statistical Pattern Recognition field. Its focus relies mainly on representation of multivariate structures.


  • Diploma in methodology of clinical research

  • Introduction to LaTeX

  • Introduction to visual analysis of information in Big Data (In Spanish)

  • Builder C++

  • MatLab