A Postgraduate certificate with multimedia content that can be accessed 24 hours a day from any device with internet connection”

New technologies have advanced exponentially in recent years causing sectors such as industry to grow enormously thanks, among other factors, to improvements in the automation of robotics. This progress has led to the creation of jobs in the field of engineering. With a positive projection the engineer faces the future in this field.

This Postgraduate certificate will allow students to acquire in-depth knowledge in the three fundamental points of Industrial Process Automation: electrical design, automation design and programming/configuration of equipment. Thus, throughout the six weeks of this program, the engineering professionals will have access to a theoretical-practical approach that will allow them to master the calculations, considerations and equipment necessary for the construction of an electrical panel, communication networks, architectures and the most modern solutions in industrial applications or industrial instrumentation.

An advanced program that will allow the students the exhaustive analysis of the programming of equipment beyond the Programmable Logic Control (PLC), with special emphasis on robots, vision equipment and drives and web interfaces. All this with a syllabus made up of multimedia material that can be accessed from the first day with an electronic device with internet connection.

An excellent opportunity for the engineering professionals who wishes to progress in their career with a flexible program that allows them to combine his work responsibilities with a quality program. A 100% online program imparted by a team of teachers specialized in the area of Robotics, which will allow you to progress in one of the current sectors on the rise. 

Access a university program that will keep you up-to-date with current machine safety standards" 

This Postgraduate certificate in Robotics in Industrial Process Automation contains the most complete and up-to-date program on the market. The most important features include:

• Development of case studies presented by experts in robotic engineering
• 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
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable electronic device with an Internet connection

An online education where you will be able to perform automation and plant simulation. Click and enroll”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its 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 education programmed to learn in real situations.

The design of this program focuses on Problem-Based Learning, by means of which the professionals must try to solve the different professional practice situations that are presented throughout the program. For this purpose, the student will be assisted by an innovative interactive video system created by renowned experts.

Master the industrial robotics of electrical actuation thanks to this Postgraduate certificate"

 Would you like to master the Programming and Configuration of Equipment in Industrial Plants? Enroll now"

Throughout the 150 teaching hours, the engineering professionals will have access to a syllabus that has been developed for students to acquire the most comprehensive knowledge in the field of Robotics in Industry. An updated syllabus made up of video summaries, specialized readings and practical cases that can be accessed in its entirety from the start of this Postgraduate certificate. A program with a theoretical-practical approach, which will lead you to master the design of automated systems, electrical design, programming and configuration of programmable logic controllers PLCs or the implementation of automation.

This Postgraduate certificate allows you to add another step forward in your career and in Industry 4.0”

Module 1. Intelligent Agents. Application of Artificial Intelligence to Robots and Softbots

1.1. Intelligent Agents and Artificial Intelligence

1.1.1. Intelligent Robots. Artificial Intelligence
1.1.2. Intelligent Agents

1.1.2.1. Hardware Agents. Robots
1.1.2.2. Software Agents. Softbots

1.1.3. Robotics Applications

1.2. Brain-Algorithm Connection

1.2.1. Biological Inspiration of Artificial Intelligence
1.2.2. Reasoning Implemented in Algorithms. Typology
1.2.3. Presentability of Results in Artificial Intelligence Algorithms
1.2.4. Evolution of Algorithms up to Deep Learning

1.3. Search Algorithms in the Solution Space

1.3.1. Elements in Solution Space Searches
1.3.2. Solution Search Algorithms in Artificial Intelligence Problems
1.3.3. Applications of Search and Optimization Algorithms
1.3.4. Search Algorithms Applied to Machine Learning

1.4. Machine Learning

1.4.1. Machine Learning
1.4.2. Supervised Learning Algorithms
1.4.3. Unsupervised Learning Algorithms
1.4.4. Reinforcement Learning Algorithms

1.5. Supervised Learning

1.5.1. Supervised Learning Methods
1.5.2. Decision Trees for Classification
1.5.3. Support Vector Machines
1.5.4. Artificial Neural Networks
1.5.5. Applications of Supervised Learning

1.6. Unsupervised Learning

1.6.1. Unsupervised Learning
1.6.2. Kohonen Networks
1.6.3. Self-Organizing Maps
1.6.4. K-Means Algorithm

1.7. Reinforcement Learning

1.7.1. Reinforcement Learning
1.7.2. Agents Based on Markov Processes
1.7.3. Reinforcement Learning Algorithms
1.7.4. Reinforcement Learning Applied to Robotics

1.8. Artificial Neural Networks and Deep Learning

1.8.1. Artificial Neural Networks. Typology
1.8.2. Applications of Neural Networks
1.8.3. Transformation from Machine Learning to Deep Learning
1.8.4.  Deep Learning Applications

1.9. Probabilistic Inference

1.9.1. Probabilistic Inference
1.9.2. Types of Inference and Method Definition
1.9.3. Bayesian Inference as a Case Study
1.9.4. Nonparametric Inference Techniques
1.9.5. Gaussian Filters

1.10. From Theory to Practice: Developing an Intelligent Robotic Agent

1.10.1. Inclusion of Supervised Learning Modules in a Robotic Agent
1.10.2. Inclusion of Reinforcement Learning Modules in a Robotic Agent
1.10.3. Architecture of a Robotic Agent Controlled by Artificial Intelligence
1.10.4. Professional Tools for the Implementation of the Intelligent Agent
1.10.5. Phases of the Implementation of AI Algorithms in Robotic Agents

Module 2. Artificial Vision Techniques in Robotics: Image Processing and Analysis

2.1. Computer Vision

2.1.1. Computer Vision
2.1.2. Elements of a Computer Vision System
2.1.3. Mathematical Tools

2.2. Optical Sensors for Robotics

2.2.1. Passive Optical Sensors
2.2.2. Active Optical Sensors
2.2.3. Non-Optical Sensors

2.3. Image Acquisition

2.3.1. Image Representation
2.3.2. Color Space
2.3.3. Digitizing Process

2.4. Image Geometry

2.4.1. Lens Models
2.4.2. Camera Models
2.4.3. Camera Calibration

2.5. Mathematical Tools

2.5.1. Histogram of an Image
2.5.2. Convolution
2.5.3. Fourier Transform

2.6. Image Preprocessing

2.6.1. Noise Analysis
2.6.2. Image Smoothing
2.6.3. Image Enhancement

2.7. Image Segmentation

2.7.1. Contour-Based Techniques
2.7.2. Histogram-Based Techniques
2.7.3. Morphological Operations

2.8. Image Feature Detection

2.8.1. Point of Interest Detection
2.8.2. Feature Descriptors
2.8.3. Feature Matching

2.9. 3D Vision Systems

2.9.1. 3D Perception
2.9.2. Feature Matching between Images
2.9.3. Multiple View Geometry

2.10. Computer Vision based Localization

2.10.1. The Robot Localization Problem
2.10.2. Visual Odometry
2.10.3. Sensory Fusion

Module 3. Robot Visual Perception Systems with Machine Learning

3.1. Unsupervised Learning Methods applied to Computer Vision

3.1.1. Clustering
3.1.2. PCA
3.1.3. Nearest Neighbors
3.1.4. Similarity and Matrix Decomposition

3.2. Supervised Learning Methods Applied to Artificial Vision

3.2.1. “Bag of Words” Concept
3.2.2. Support Vector Machine
3.2.3. Latent Dirichlet Allocation
3.2.4. Neural Networks

3.3. Deep Neural Networks: Structures, Backbones and Transfer Learning

3.3.1. Feature Generating Layers

3.3.1.1. VGG
3.3.1.2. Densenet
3.3.1.3. ResNet
3.3.1.4. Inception
3.3.1.5. GoogLeNet

3.3.2. Transfer Learning
3.3.3. The Data Preparation for Training

3.4. Artificial Vision with Deep Learning I: Detection and Segmentation

3.4.1. YOLO and SSD Differences and Similarities
3.4.2. Unet
3.4.3. Other Structures

3.5. Computer Vision with Deep Learning II: Generative Adversarial Networks

3.5.1. Image Super-Resolution Using GAN
3.5.2. Creation of Realistic Images
3.5.3. Scene Understanding

3.6. Learning Techniques for Localization and Mapping in Mobile Robotics

3.6.1. Loop Closure Detection and Relocation
3.6.2. Magic Leap. Super Point and Super Glue
3.6.3. Depth from Monocular

3.7. Bayesian Inference and 3D Modeling

3.7.1. Bayesian Models and "Classical" Learning
3.7.2. Implicit Surfaces with Gaussian Processes (GPIS)
3.7.3. 3D Segmentation Using GPIS
3.7.4. Neural Networks for 3D Surface Modeling

3.8. End-to-End Applications of Deep Neural Networks

3.8.1. End-to-End System. Example of Person Identification
3.8.2. Object Manipulation with Visual Sensors
3.8.3. Motion Generation and Planning with Visual Sensors

3.9. Cloud Technologies to Accelerate the Development of Deep Learning Algorithms

3.9.1. Use of GPUs for Deep Learning
3.9.2. Agile Development with Google Colab
3.9.3. Remote GPUs, Google Cloud and AWS

3.10. Deployment of Neural Networks in Real Applications

3.10.1. Embedded Systems
3.10.2. Deployment of Neural Networks. Use
3.10.3. Network Optimizations in Deployment, Example with TensorRT.

Enroll now and acquire the most advanced knowledge in the implementation of automation systems”