This program in Artificial Intelligence in Programming will provide you with a holistic perspective on how AI impacts and improves every stage of software development”

The importance of Artificial Intelligence in Programming lies in its ability to empower and automate processes, optimizing software development and improving efficiency in solving complex problems. Its ability to analyze large volumes of data and find optimal solutions has led to significant advances in fields such as the optimization of algorithms, the creation of more intuitive interfaces and the resolution of complex problems in different areas

That is why TECH has developed this professional master’s degree, which emerges as a strategic solution to amplify the professional opportunities and career growth of computer scientists. It will address the improvement of productivity in software development through AI, exploring techniques and tools that automate processes, optimize code and accelerate the creation of intelligent applications

In addition, the program will focus on the crucial role of AI in the field of QA Testing, implementing AI algorithms and methods to improve test quality, accuracy and coverage, detecting and correcting errors more efficiently. It will also delve into the integration of machine learning and natural language processing capabilities in web development, creating intelligent sites that adapt and offer personalized experiences to users

Furthermore, it will delve into AI techniques to improve the usability, interaction and functionality of mobile applications, to create intelligent and predictive applications that adapt to user behavior. Likewise, software architecture with AI will be analyzed in depth, including the various models that will facilitate the integration of AI algorithms and their deployment in production environments

With the purpose of nurturing highly competent AI specialists, TECH has conceived a comprehensive program based on the unique Relearning methodology. This approach will allow students to consolidate their understanding through repetition of fundamental concepts

You will lead innovative projects adapted to the demands of a constantly evolving technology market” What are you waiting for to enroll?"

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

• Development of practical cases presented by experts in Artificial Intelligence in Programming
• Graphic, schematic, and practical contents which 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

You will dive into the fundamentals of software architecture, including performance, scalability and maintainability, thanks to the most innovative multimedia resources”

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 course. For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts

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

You will delve into the integration of AI elements in Visual Studio Code and code optimization with ChatGPT, all through a comprehensive educational program.

The main objective of this program will be to provide professionals with access to the most cutting-edge knowledge in the field, with an approach that promotes their comprehensive education. As such, they will have the opportunity to participate in an exclusive and fully online educational pathway. Graduates will be equipped with useful cutting-edge skills, from AI-powered software development to the design and execution of web projects and mobile applications with intelligence and adaptability. With this program, the computer scientist will transcend the boundaries of conventional programming and become an active player in the technological revolution

You will tackle the testing life cycle, from the creation of test cases to the detection of bugs, thanks to TECH"

General Objectives

• Develop skills to configure and manage efficient development environments, ensuring a solid foundation for the implementation of AI projects
• Acquire skills in planning, executing and automating quality testing, incorporating AI tools for bug detection and correction
• 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 training
Apply practical guidelines to ensure efficient and effective training 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 Recurrent Neural Networks (RNN) and Attention

Developing 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. Software Development Productivity Improvement 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 configuration of optimized development environments, ensuring that students are able to create environments conducive to AI projects
Apply specific techniques using ChatGPT for the automatic identification and correction of potential code improvements, encouraging more efficient programming practices
Promote collaboration between professionals from different programmers (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 testing and ensuring software quality
Recognize and analyze different types of software frameworks, such as monolithic, microservices or service oriented
Gain a comprehensive vision 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. Website Projects with AI

Develop comprehensive skills for the implementation of web projects, from frontend design to backend optimization, 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. Mobile Applications with AI

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 the configuration of the mobile app framework and use 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 bug detection and continuous software improvement

You will master the technologies of the future with this exclusive 100% online university program. Only with TECH!"