Be the expert Robotics Engineer that every company wants to have in its team. Enroll now”

Any autonomous robot capable of navigating must provide the mechanisms that provide answers to fundamental questions such as: Where am I, where do I want to go, and how do I get there? This Postgraduate diploma provides engineers with the knowledge and current technological tools used to answer these questions and propel career in this field.

Due to the high capabilities and complexity of artificial intelligence algorithms, it is essential to master this subject in order to successfully deal with this technology. The specialized teaching team in charge of providing this education will accompany students in this journey to achieve their professional goals with solvency.

This program, taught entirely in online mode, will cover one of the key aspects in the field of robot autonomy, artificial vision. The different architectures, uses of deep neural networks and 2D and 3D vision problems will be covered in this Postgraduate diploma.

An excellent opportunity for the engineering professionals who want to specialize in a booming industry with a wide range of job opportunities. All of this with a learning system that facilitates the acquisition of a specialization without neglecting personal responsibilities thanks to the absence of fixed schedules to access all the content of the program. Thus, students only need an electronic device with internet connection to access the platform and start at any time of the day.in a program that will boost their career.

Specialize and succeed in the Robotics industry. Take the step and sign up’’

This Postgraduate diploma in Robot Visual Perception Systems with Machine Learning 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 device with an Internet connection

You have the opportunity to advance in a booming field. Enroll and perfect your knowledge in Artificial Intelligence”

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.

The multimedia resource library of this Postgraduate diploma provides you with a cutting-edge and highly useful content for your career"

Acquire learning that will lead to the optimal deployment of Neural Networks in Real Applications"

The teaching team involved in the development of this 100% online program has developed a syllabus where students will be immersed in the application of Artificial Intelligence to robots and Softbots and in the improvement of the visual perception of robots through the main techniques and tools used to achieve Machine Learning. The videos in detail of each topic provided by the professionals who teach this course will facilitate learning. Likewise, the students will have the whole syllabus at their disposal from the beginning of the program, which will allow them distributing the teaching load according to the student's needs.

Enroll now and learn in this Postgraduate diploma with the best specialists in Robotics”

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

Take the step to get up to date on the latest developments in Robot Visual Perception Systems with Machine Learning"