With this professional master’s degree you will discover how Artificial Intelligence is transforming industries and you will prepare yourself to lead the change"

AI is transforming numerous industries, from healthcare to logistics to automotive to e-commerce. Its ability to automate repetitive tasks and improve efficiency has generated a growing demand for professionals capable of mastering different types of machine learning algorithms. In such a new and constantly evolving sector, it is imperative to stay up-to-date in order to compete in an increasingly technology-driven job market.

For this reason, TECH has developed a program that is presented as a strategic response to improve the job prospects and promotion potential of engineers. n this way, an innovative syllabus has been developed in which students will delve into the fundamentals of AI and delve into data mining.

Throughout the development of this professional master’s degree, graduates will immerse themselves in the essential foundations, tracing the historical evolution of AI and exploring its future projections. In this way, they will delve into their integration into mass-use applications, to understand how these platforms improve the user experience and optimize operational efficiency.

It is therefore an exclusive academic program, thanks to which professionals can develop optimization processes inspired by biological evolution, finding and applying efficient solutions to complex problems with the in-depth mastery of Artificial Intelligence.

To facilitate the integration of new knowledge, TECH has created this complete program based on the exclusive Relearningmethodology. Under this approach, students will reinforce understanding through repetition of key concepts throughout the program, which will be presented in various audiovisual supports for a progressive and effective knowledge acquisition. All this from an innovative and flexible system, totally online, that allows to adapt the learning to the schedules of the participants.

Boost your professional profile by developing advanced AI-, based solutions, with the most comprehensive program in the digital academic landscape"

This professional master’s degree in Artificial Intelligence contains the most complete and up-to-date program on the market. The most important features include:

• Development of practical cases presented by experts in Artificial Intelligence
• The graphic, schematic and practical contents with which it is conceived provide cutting- Therapeutics and practical information on those 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 will address from the evolution of neural networks to Deep Learning and you will acquire solid competencies in the implementation of advanced Artificial Intelligence solutions"

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.

You will optimize the potential of data storage in the best digital university in the world , according to Forbes"

You will be able to access exclusive content on the virtual campus 24 hours a day, with no geographical or time restrictions"

The many advances that have been made in the field of Artificial Intelligence have generated a need for constant updating on the part of professionals. For this reason, TECH has created a unique and complete program , with which graduates will master the complex algorithms that make Artificial Intelligence 'come to life'. The final goal of this degree will be to make available to students the latest information in the sector, with a training and avant-garde approach. In this way, the graduate will access a unique academic itinerary, taught 100% online.

You will master the keys to information hidden in large data sets and increase your job visibility in an ever-expanding market"

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle
• Evaluate the crucial role of data in the development and implementation of AI solutions
• Delve into algorithms and complexity to solve specific problems
• Explore the theoretical basis of neural networks for Deep Learning development
• Explore bio-inspired computing and its relevance in the development of intelligent systems
• Analyze current strategies of Artificial Intelligence in various fields, identifying opportunities and challenges

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
• Understanding 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
• Tuning 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 Natural Recurrent Networks (NNN) 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

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