Develop essential skills in Robotics and Computer Vision by enrolling now in this TECH Grand Master”

The rise of Artificial Intelligence and Robotics is transforming the technological, economic and social landscape worldwide. Specialization in areas such as Machine Vision has become crucial to stay ahead in this era of rapid advances and disruptive change. The increasing interaction between machines and humans, as well as the need to process visual information efficiently, require highly trained professionals who can address these challenges and lead innovation.

The advanced master’s degree in Robotics and Computer Vision offers comprehensive training in these emerging disciplines, covering topics such as Augmented Reality, Artificial Intelligence and visual information processing in machines, among others. Students will benefit from a theoretical-practical approach, learning about the latest developments in Robotics and Computer Vision and how to apply this knowledge in real environments.

Furthermore, the program is 100% online, which allows students to adapt their learning to their personal and professional circumstances, facilitating the compatibility of their education with their own responsibilities. Students will have access to high quality educational materials, such as video summaries, essential readings and in-depth videos, providing them with a comprehensive overview of Robotics and Machine Vision.

Thus, The advanced master’s degree in Robotics and Computer Vision is a unique opportunity for computer scientists looking to differentiate themselves in a highly competitive job market and acquire specialized skills in a field with great growth potential.

Master machine vision techniques and become an expert in image analysis and 3D vision systems”

This advanced master’s degree in Robotics and Computer Vision contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by IT experts
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Special emphasis on innovative methodologies in the development of and Computer Vision
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Discover how robotic technology can be applied in diverse fields such as medicine and space exploration, considerably strengthening your value proposition”

Its teaching staff includes professionals from the field of journalism, who bring to this program the experience of their work, as well as renowned specialists from reference societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.

Empower your projects by exploring the application of artificial intelligence and machine learning in robotics"

Strengthen your skills in planning and control algorithms for the development of intelligent and efficient robots"

The advanced master’s degree in Robotics and Computer Vision's main objective is to train experts in the field of robotics, providing a solid theoretical and practical foundation in essential areas such as computer vision, mobile robotics and artificial intelligence applied to robotics. Students will learn to design and develop advanced robotic systems that are efficient and collaborative, enhancing human-robot interaction and ensuring safety in diverse environments.

Delve into key areas of robotics and become an expert in creating innovative solutions"

General Objectives

• Understand the mathematical foundations for kinematic and dynamic modeling of robots
• Delve into the use of specific technologies for the creation of robot architectures, robot modeling and simulation
• Generate specialized knowledge on Artificial Intelligence
• Develop the technologies and devices most commonly used in industrial automation
• Identify the limits of current techniques to identify bottlenecks in robotic applications
• Obtain an overview of the devices and hardware used in the computer vision world
• Analyze the different fields in which vision is applied
• Identify where the technological advances in vision are at the moment
• Assess what is being researched and what the next few years hold
• Establish a solid foundation in the understanding of digital image processing algorithms and techniques
• Assess fundamental computer vision techniques
• Analyze advanced image processing techniques
• Introducing the open 3D library
• Analyze the advantages and difficulties of working in 3D instead of 2D
• Introduce neural networks and examine how they work
• Analyze metrics for proper learning
• Analyze existing metrics and tools
• Examine the pipeline of an image classification network
• Analyze semantic segmentation neural networks and their metrics

Specific Objectives

Module 1. Robotic. Robot Design and Modeling

• Delve into the use of Gazebo Simulation Technology
• Master the use of the URDF Robot Modeling language
• Develop specialized knowledge in the use of Robot Operating System technology
• Model and Simulate Manipulator Robots, Land Mobile Robots, Air Mobile Robots and Model and Simulate Aquatic Mobile Robots

Module 2. Intelligent Agents. Applying Artificial Intelligence to Robots and Softbots

• Analyze the biological inspiration of Artificial Intelligence and intelligent agents
• Assess the need for intelligent algorithms in today's society
• Determine the applications of advanced Artificial Intelligence techniques on Intelligent Agents
• Demonstrate the strong connection between Robotics and Artificial Intelligence
• Establish the needs and challenges presented by Robotics that can be solved with Intelligent Algorithms
• Develop concrete implementations of Artificial Intelligence Algorithms
• Identify Artificial Intelligence algorithms that are established in today's society and their impact on daily life

Module 3. Deep Learning

• Analyze the families that make up the artificial intelligence world
• Compile the main Frameworks of Deep Learning
• Define neural networks
• Present the learning methods of neural networks
• Fundamentals of cost functions
• Establish the most important activation functions
• Examine regularization and normalization techniques
• Develop optimization methods
• Introduce initialization methods

Module 4. Robotics in the Automation of Industrial Processes

• Analyze the use, applications and limitations of industrial communication networks
• Establish machine safety standards for correct design
• Develop clean and efficient programming techniques in PLCs
• Propose new ways of organizing operations using state machines
• Demonstrate the implementation of control paradigms in real PLC applications
• Fundamentalize the design of pneumatic and hydraulic installations in automation
• Identify the main sensors and actuators in robotics and automation

Module 5. Automatic Control Systems in Robotics

• Generate specialized knowledge for the design of nonlinear controllers
• Analyze and study control problems
• Master control models
• Design nonlinear controllers for robotic systems
• Implement controllers and assess them in a simulator
• Determine the different existing control architectures
• Examine the fundamentals of vision control
• Develop state-of-the-art control techniques such as predictive control or machine learning based control

Module 6. Robot Planning Algorithms

• Establish the different types of planning algorithms
• Analyze the complexity of motion planning in robotics
• Develop techniques for environment modeling
• Examine the pros and cons of different planning techniques
• Analyze centralized and distributed algorithms for robot coordination
• Identify the different elements in decision theory
• Propose learning algorithms for solving decision problems

Module 7. Computer Vision

• Establish how the human vision system works and how an image is digitized
• Analyze the evolution of computer vision
• Evaluate image acquisition techniques
• Generate specialized knowledge about illumination systems as an important factor when processing an image
• Specify what optical systems exist and evaluate their use
• Examine the 3D vision systems and how these systems provide depth to images
• Develop the different existing systems outside the field visible to the human eye

Module 8. Applications and State-of-the-Art

• Analyze the use of computer vision in industrial applications
• Determine how vision is applied in the autonomous vehicle revolution
• Analyze images in content analysis
• Develop Deep Learning algorithms for medical analysis and Machine Learning algorithms for operating room assistance
• Analyze the use of vision in commercial applications
• Determine how robots have eyes thanks to computer  vision and how it is applied in space travel
• Establish what augmented reality is and fields of use
• Analyze the Cloud Computing revolution
• Present the State of the Art and what the coming years have in store for us

Module 9. Computer Vision Techniques in Robotics: Image Processing and Analysis

• Analyze and understand the importance of vision systems in robotics
• Establish the characteristics of the different perception sensors in order to choose the most appropriate ones according to the application
• Determine the techniques for extracting information from sensor data
• Apply visual information processing tools
• Design digital image processing algorithms
• Analyze and predict the effect of parameter changes on algorithm performance
• Assess and validate the developed algorithms in terms of results

Module 10. Robot Visual Perception Systems with Machine Learning

• Master the machine learning techniques most widely used today in academia and industry
• Delve into the architectures of neural networks to apply them effectively in real problems
• Reuse existing neural networks in new applications using transfer learning
• Identify new fields of application of generative neural networks
• Analyze the use of learning techniques in other fields of robotics such as localization and mapping
• Develop current technologies in the cloud to develop neural network-based technologies
• Examine the deployment of vision learning systems in real and embedded systems

Module 11. Visual SLAM. Robot Localization and Simultaneous Mapping Using Computer Vision Techniques

• Specify the basic structure of a Simultaneous Localization and Mapping (SLAM) system
• Identify the basic sensors used in Simultaneous Localization and Mapping (visual SLAM)
• Establish the boundaries and capabilities of visual SLAM
• Compile the basic notions of projective and epipolar geometry to understand imaging projection processes
• Identify the main visual SLAM technologies: Gaussian filtering, Optimization and loop closure detection
• Describe in detail the operation of the main visual SLAM algorithms
• Analyze how to carry out the tuning and parameterization of SLAM algorithms

Module 12. Application of Virtual and Augmented Reality Technologies to Robotics

• Determine the difference among the different types of realities
• Analyze the current standards for modeling virtual elements
• Examine the most commonly used peripherals in immersive environments
• Define geometric models of robots
• Assess physics engines for dynamic and kinematic modeling of robots
• Develop Virtual Reality and Augmented Reality projects

Module 13. Robot Communication and Interaction Systems

• Analyze current natural language processing strategies: heuristic, stochastic, neural network-based, reinforcement-based learning
• Assess the benefits and weaknesses of developing cross-cutting, or situation-focused, interaction systems
• Identify the environmental problems to be solved in order to achieve effective communication with the robot
• Establish the tools needed to manage the interaction and discern the type of dialogue initiative to be pursued
• Combine pattern recognition strategies to infer the intentions of the interlocutor and respond in the best way to them
• Determine the optimal expressiveness of the robot according to its functionality and environment, and apply emotional analysis techniques to adapt its response
• Propose hybrid strategies for interaction with the robot: vocal, tactile and visual

Module 14. Digital Image Processing

• Examine commercial and open-source digital image processing libraries
• Determine what a digital image is and evaluate the fundamental operations to be able to work with them
• Introduce image filters
• Analyze the importance and use of histograms
• Present tools to modify images pixel by pixel
• Propose image segmentation tools
• Analyze morphological operations and their applications
• Determine the methodology in image calibration
• Evaluate methods for segmenting images with conventional vision

Module 15. Advanced Digital Image Processing

• Examine advanced digital image processing filters
• Determine contour extraction and analysis tools
• Analyze object search algorithms
• Demonstrate how to work with calibrated images
• Analyze mathematical techniques for geometry analysis
• Evaluate different options in image compositing
• Develop user interface

Module 16. 3D Image Processing

• Examine a 3D image
• Analyze the software used for 3D data processing
• Developing open3D
• Determine the relevant data in a 3D image
• Demonstrate visualization tools
• Establish denoising filters
• Propose Geometric Calculation tools
• Analyze object detection methodologies
• Evaluate triangulation and scene reconstruction methods

Module 17. Convolutional Neural Networks and Image Classification

• Generate specialized knowledge on convolutional neural networks
• Establish evaluation metrics
• Analyze the performance of CNNs for image classification
• Evaluate Data Augmentation
• Propose techniques to avoid Overfitting
• Examine different architectures
• Compile inference methods

Module 18. Object Detection

• Analyze how object detection networks work
• Examine traditional methods
• Determine evaluation metrics
• Identify the main datasets used in the marketplace
• Propose architectures of the Two Stage Object Detector type
• Analyze Fine Tuning Methods
• Examine different Single Shoot type architectures
• Establish object tracking algorithms
• Apply detection and tracking of people

Module 19. Image Segmentation with Deep Learning

• Analyze how semantic segmentation networks work
• Evaluate traditional methods
• Examine evaluation metrics and different architectures
• Examine video domains and cloud points
• Apply theoretical concepts through various examples

Module 20. Advanced Image Segmentation and Advanced Computer Vision Techniques

• Generate specialized knowledge on the handling of tools
• Examine Semantic Segmentation in medicine
• Identify the structure of a segmentation project
• Analyze Autoencoders
• Develop Adversarial Generative Networks

Get ready to face the robotics challenges of the future and contribute to the advancement of technology in various sectors”