You will analyze how AI interprets genetic data to design specific therapeutic strategies, thanks to this 100% online program”

Big Data analysis significantly improves medical care and healthcare research. Such advanced systems provide experts with the opportunity to personalize treatments. Patient information such as medical history, genetics or lifestyle are used to tailor therapeutic plans and medications individually. In addition, these tools contribute to continuous monitoring of patients outside the clinical setting, which is especially beneficial for users suffering from chronic conditions. Therefore, AI resources contribute to the development of more effective and care-enhancing care management procedures.

For this reason, TECH has designed a professional master’s degree that will delve into the analysis of Big Data and Machine Learning in Clinical Research. The syllabus will address aspects such as data mining in both clinical and biomedical records, while focusing on algorithms and providing predictive analysis techniques. Moreover, the program will explore the interactions that occur in biological networks for the identification of disease patterns. In addition, the curriculum will pay careful attention to the ethical and legal factors of AI in the medical context. In this way, graduates will gain a responsible conscience when carrying out their procedures.

It should be noted that, in order to consolidate all these contents, TECH relies on the revolutionary Relearning methodology. This teaching system is based on the reiteration of key concepts in order to consolidate an optimal understanding. The only requirement for students is to have an electronic device (such as a cell phone, computer or Tablet) connected to the Internet, in order to access the Virtual Campus and view the contents at any time. In this way, they will learn from the comfort of their homes, forgetting about classroom attendance and pre-established schedules.

You will master TensorFlow Datasets for data loading and achieve efficient medical data preprocessing thanks to this program”

This professional master’s degree in Artificial Intelligence in Clinical Practice 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 in Clinical Practice
• 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 will be at the forefront of the medical field! This program merges clinical excellence with the technological revolution of Machine Learning”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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.

The modular structure of the program will allow you a coherent progression, from the fundamentals to the most advanced applications"

Forget about memorizing! With the Relearning system you will integrate the concepts in a natural and progressive way"

This professional master’s degree will turn students into true leaders, capable of overcoming current and future challenges in medicine. Graduates will have a thorough understanding of AI, which will contribute to developing innovative solutions to transform medical care. In this way, professionals will apply medical data analysis techniques, the development of predictive models for clinical trials and the implementation of innovative solutions for the personalization of treatments.

Update your daily clinical practice to be at the forefront of the technological revolution in health, contributing to the advancement of Clinical Practice"

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
• Analyze 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
• Gain a comprehensive view of the transformation of Clinical Research through AI, from its historical foundations to current applications
• Learn effective methods for integrating heterogeneous data into Clinical Research, including natural language processing and advanced data visualization
• Acquire solid knowledge of model validation and simulations in the biomedical domain, exploring the use of synthetic datasets and practical applications of AI in health research
• Understand and apply genomic sequencing technologies, data analysis with AI and use of AI in biomedical imaging
• Acquire expertise in key areas such as personalization of therapies, precision medicine, AI-assisted diagnostics and clinical trial management
• Gain a solid understanding of the concepts of Big Data in the clinical setting and become familiar with essential tools for its analysis
• Delve into ethical dilemmas, review legal considerations, explore the socioeconomic and future impact of AI in healthcare, and promote innovation and entrepreneurship in the field of clinical AI

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 learning
• 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 learning
• 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.

• 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. Diagnosis in Clinical Practice Using AI

• Critically analyze the benefits and limitations of AI in health care
• Identify potential pitfalls, providing an informed assessment of its application in clinical settings
• Recognize the importance of collaboration across disciplines to develop effective AI solutions
• Develop competencies to apply AI tools in the clinical setting, focusing on aspects such as assisted diagnosis, medical image analysis and interpretation of results
• Identify potential pitfalls in the application of AI in healthcare, providing an informed view of its use in clinical settings

Module 17. Treatment and Management of the AI Patient

• Interpret results for ethical dataset creation and strategic application in healthcare emergencies
• Acquire advanced skills in the presentation, visualization and management of healthcare AI data
• Gain a comprehensive perspective on emerging trends and technological innovations in AI applied to healthcare
• Develop AI algorithms for specific applications such as health monitoring, facilitating the effective implementation of solutions in medical practice
• Design and implement individualized medical treatments by analyzing patients' clinical and genomic data with AI

Module 18. Health Personalization through AI

• Delve into emerging trends in AI applied to personalized healthcare and their future impact
• Define the applications of AI to personalize medical treatments, ranging from genomic analysis to pain management
• Differentiate specific AI algorithms for the development of applications related to drug design or surgical robotics
• Delineate emerging trends in AI applied to personalized health and their future impact
• Promote innovation through the creation of strategies aimed at improving medical care

Module 19. Analysis of Big Data in the Healthcare Sector with AI

• Acquire solid knowledge in medical data procurement, filtering, and preprocessing
• Develop a clinical approach based on data quality and integrity in the context of privacy regulations
• Apply the acquired knowledge in use cases and practical applications, enabling to understand and solve industry-specific challenges, from text analytics to data visualization and medical information security
• Define Big Data techniques specific to the healthcare sector, including the application of machine learning algorithms for analytics
• Employ Big Data procedures to track and monitor the spread of infectious diseases in real time for effective response to epidemics

Module 20. Ethics and Regulation in Medical AI

• Understand the fundamental ethical principles and legal regulations applicable to the implementation of AI in medicine
• Master the principles of data governance
• Understand international and local regulatory frameworks
• Ensure regulatory compliance in the use of AI data and tools in the healthcare sector
• Develop skills to design human-centered AI systems, promoting fairness and transparency in machine learning

Enjoy the most up-to-date educational content available in innovative multimedia formats to optimize your studies"