You will be able to design customized and intuitive user experiences through this 100% online university degree"

Computational Intelligence serves institutions to improve productivity in software development. Its tools have the ability to handle unstructured data, learn from past experiences and adapt to changes in dynamic environments. In addition, AI can predict potential application problems before they happen, allowing professionals to take preventative measures to avoid costly problems in the future. In this context, the most prestigious international IT companies are looking to actively incorporate Software Architecture specialists for QA Testing.

For this reason, TECH implements an innovative program for programmers to get the most out of optimization and performance management in AI tools. Designed by world-class experts, the curriculum will delve into programming algorithms to develop products with intelligent systems. The syllabus will also delve into the essential extensions for Visual Studio Code, today's most widely used source code editor. Moreover, the teaching materials will address the integration of AI in database management to detect possible failures and create unittests This is a university degree that has a diversity of audiovisual content in multiple formats and a network of real simulations to bring the development of the program closer to the reality of IT practice.

In order to achieve the proposed learning objectives, this program is taught through an online teaching methodology. In this way, professionals will be able to perfectly combine their work with their studies. In addition, you will enjoy a first-class teaching staff and multimedia academic materials of great pedagogical rigor such as master classes, interactive summaries or practical exercises. The only requirement for accessing the Virtual Campus is that students have an electronic device with Internet access, and can even use their cell phone.

You will gain a holistic perspective on how Machine Learning impacts and improves every stage of software development"

This professional master’s degree in Artificial Intelligence in Programming contains the most complete and up-to-date program on the market. Its most notable features are:

• The development of practical cases presented by experts in Artificial Intelligence in programming
• 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 the self-assessment process can be carried out 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 

Are you looking to apply Transformational Models for natural language processing to your practice? Achieve it thanks to this innovative program"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading 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 immersive education programmed to learn in real situations.

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

You will delve into the testing lifecycle, from the creation of test cases to the detection of bugs"

Relearning will enable you to learn with less effort and more performance, involving you more in your professional specialization"

This program will turn computer scientists into experts in AI applied to programming. Graduates will acquire a comprehensive vision that combines the most updated knowledge with practical skills that will improve their decision making. At the same time, professionals will master the most modern tools for the development of software powered by Machine Learning. In this way, students will design proposals for both webs and mobile applications with adaptability. They will be highly specialized to meet the current demands of the industry.

Looking to specialize in Artificial Intelligence? With this program you will master the optimization of the deployment process and the integration of Artificial Intelligence in cloud computing" 

General Objectives

• Develop skills to set up and manage efficient development environments, ensuring a solid foundation for the implementation of AI projects 
• Acquire skills in planning, executing and automating quality tests, incorporating AI tools for bug detection and remediation 
• Understand and apply performance, scalability and maintainability principles in the design of large-scale computing systems  
• Become familiar with the most important design patterns and apply them effectively in software architecture 

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence  

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Explore the concept of the semantic web and its influence on the organization and understanding of information in digital environments

Module 2. Data Types and Data Life Cycle  

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data 
• Analyze the life cycle of data, from generation to disposal, identifying key stages 
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure 
• Study data collection processes, including methodology, tools and collection channels 
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design 
• Analyze the regulatory aspects related to data management, complying with privacy and security regulations, as well as best practices

Module 3. Data in Artificial Intelligence  

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Analyze supervised and unsupervised models, including methods and classification 
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence 

Module 4. Data Mining. Selection, Pre-Processing and Transformation 

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends 
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining 
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context 
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set 
• Address data preprocessing in Big Data environments 

Module 5. Algorithm and Complexity in Artificial Intelligence  

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving 
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space 
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts 
• Explore tree-based algorithms, understanding their structure and applications 
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation 
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships 
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems  

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems 
• Study semantic reasoners, knowledge-based systems and expert systems, understanding their functionality and applications in intelligent decision making

Module 7: Machine Learning and Data Mining 

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems 
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers 
• Analyze regression and continuous response models for predicting numerical values from data 
• Study clustering techniques to identify patterns and structures in unlabeled data sets 
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text 

Module 8. Neural networks, the basis of Deep Learning  

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning 
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately 
• Understand the effective linking of layers and operations to design complex and efficient neural network architectures 
• Use trainers and optimizers to tune and improve the performance of neural networks 
• Explore the connection between biological and artificial neurons for a deeper understanding of model design 
• Tune hyperparameters for Fine Tuning of neural networks, optimizing their performance on specific tasks 

Module 9. Deep Neural Networks Training  

• Solve gradient-related problems in deep neural network training 
• Explore and apply different optimizers to improve the efficiency and convergence of models 
• Program the learning rate to dynamically adjust the convergence speed of the model 
• Understand and address overfitting through specific strategies during the course 
• Apply practical guidelines to ensure efficient and effective learning of deep neural networks 
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks 
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization 
• Develop practical applications using Transfer Learning to solve real-world problems 
• Understand and apply regularization techniques to improve generalization and avoid overfitting in deep neural networks 

Module 10. Model Customization and Training with TensorFlow  

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow 
• Explore the tfdata API to efficiently manage and manipulate datasets 
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow 
• Use Keras preprocessing layers to facilitate the construction of custom models 
• Explore the TensorFlow Datasets project to access predefined datasets and improve development efficiency 
• Develop a Deep Learning  application with TensorFlow, integrating the knowledge acquired in the module 
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations 

Module 11. Deep Computer Vision with Convolutional Neural Networks  

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision 
• Explore and apply convolutional layers to extract key features from images 
• Implement clustering layers and their use in  Deep Computer Vision models with Keras 
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts 
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance 
• Use pre-trained Keras models to leverage transfer learning for specific tasks 
• Apply classification and localization techniques in Deep Computer Vision environments 
• Explore object detection and object tracking strategies using Convolutional Neural Networks 
• Implement semantic segmentation techniques to understand and classify objects in images in a detailed manner 

Module 12. Natural Language Processing (NLP) with Natural Recurrent Networks (NRN) and Attention

• Develop skills in text generation using Recurrent Neural Networks (RNN) 
• Apply RNNs in opinion classification for sentiment analysis in texts 
• Understand and apply attentional mechanisms in natural language processing models 
• Analyze and use Transformers models in specific NLP tasks 
• Explore the application of Transformers models in the context of image processing and computer vision 
• Become familiar with the Hugging Face Transformers library for efficient implementation of advanced models 
• Compare different Transformers libraries to evaluate their suitability for specific tasks 
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems 

Module 13. Autoencoders, GANs , and Diffusion Models  

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation 
• Implement and understand the operation of stacked autoencoders 
• Explore and apply convolutional autoencoders for efficient visual data representations 
• Analyze and apply the effectiveness of sparse automatic encoders in data representation 
• Generate fashion images from the MNIST dataset using Autoencoders 
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models 
• Implement and compare the performance of Diffusion Models and GANs in data generation 

Module 14. Bio-Inspired Computing   

• Introduce the fundamental concepts of bio-inspired computing
• Explore social adaptation algorithms as a key approach in bio-inspired computing 
• Analyze space exploration-exploitation strategies in genetic algorithms 
• Examine models of evolutionary computation in the context of optimization  
• Continue detailed analysis of evolutionary computation models  
• Apply evolutionary programming to specific learning problems 
• Address the complexity of multi-objective problems in the framework of bio-inspired computing 
• Explore the application of neural networks in the field of bio-inspired computing  
• Delve into the implementation and usefulness of neural networks in bio-inspired computing 

Module 15. Artificial Intelligence: Strategies and Applications 

• Develop strategies for the implementation of artificial intelligence in financial services
• Analyze the implications of artificial intelligence in the delivery of healthcare services 
• Identify and assess the risks associated with the use of AI in the healthcare field 
• Assess the potential risks associated with the use of AI in industry 
• Apply artificial intelligence techniques in industry to improve productivity 
• Design artificial intelligence solutions to optimize processes in public administration 
• Evaluate the implementation of AI technologies in the education sector 
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity 
• Optimize human resources processes through the strategic use of artificial intelligence 

Module 16. Improving Software Development Productivity with AI  

• Delve into the implementation of must-have AI extensions in Visual Studio Code to improve productivity and facilitate software development
• Gain a solid understanding of basic AI concepts and their application in software  development, including machine learning algorithms, natural language processing, neural networks, etc 
• Master the setup of optimized development environments, ensuring that students are able to create environments conducive to AI projects 
• Apply specific techniques using ChatGPT for automatic identification and correction of potential code improvements, encouraging more efficient programming practices 
• Promote collaboration between different programming professionals (from programmers to data engineers to user experience designers) to develop effective and ethical AI software solutions 

Module 17. Software Architecture for QA Testing 

• Develop skills to design solid test plans, covering different types of testingand ensuring software quality
• Recognize and analyze different types of software frameworks, such as monolithic, microservices or service-oriented 
• Gain a comprehensive view on the principles and techniques for designing computer systems that are scalable and capable of handling large volumes of data 
• Apply advanced skills in the implementation of AI-powered data structures to optimize software  performance and efficiency  
• Develop secure development practices, with a focus on avoiding vulnerabilities to ensure software security at the architectural level 

Module 18. Web Projects with AI 

• Develop comprehensive skills for the implementation of web projects, from  frontend design tobackendoptimization, with the inclusion of AI elements
• Optimize the process of deploying websites, incorporating techniques and tools to improve speed and efficiency 
• Integrate AI into cloud computing, enabling students to create highly scalable and efficient web projects 
• Acquire the ability to identify specific problems and opportunities in web projects where AI can be effectively applied, such as in text processing, personalization, content recommendation, etc
• Encourage students to keep abreast of the latest trends and advances in AI for its proper application in web projects 

Module 19. AI-enabled Mobile Applications 

• Apply advanced concepts of clean architecture, datasources and repositories to ensure a robust and modular structure in AI-enabled mobile applications 
• Develop skills to design interactive screens, icons and graphical resources using AI to enhance the user experience in mobile applications 
• Delve into mobile app framework configuration and employ Github Copilot to streamline the development process 
• Optimize mobile applications with AI for efficient performance, taking into account resource management and data usage 
• Perform quality testing of AI mobile applications, enabling students to identify problems and debug bugs  

Module 20. AI for QA Testing 

• Master principles and techniques for designing computer systems that are scalable and capable of handling large volumes of data 
• Apply advanced skills in the implementation of AI-powered data structures to optimize software  performance and efficiency 
• Understand and apply secure development practices, with a focus on avoiding vulnerabilities such as injection, to ensure software security at the architectural level 
• Generate automated tests, especially in web and mobile environments, integrating AI tools to improve process efficiency 
• Use advanced AI-powered QA tools for more efficient bugdetection and continuous software improvement  

You will delve into the integration of Visual Studio Code elements and code optimization with ChatGPT, through a comprehensive academic program"