Thanks to this 100% online Professional master’s degree, you will acquire advanced technological skills, through AI, to optimize talent management and improve operational efficiency in your organization”

Artificial Intelligence (AI) is revolutionizing the Human Resources (HR) department, improving efficiency in talent management and decision making. AI-based tools, such as chatbots and sentiment analysis software, enable more fluid interaction with employees and help identify needs before they become problems.

This is how this Professional master’s degree was created, thanks to which professionals will be able to improve operational efficiency in personnel administration by automating tasks such as resource allocation and payroll management. In addition, they will delve into predictive analysis to anticipate staffing needs and the integration of systems that ensure impeccable regulatory compliance.

Advanced tools to automate the analysis of resumes and the classification of candidates will be mastered, as well as virtual interviews assisted by Artificial Intelligence. Techniques to eliminate biases in personnel selection will also be addressed, ensuring a fairer and more accurate recruitment process, increasing the retention and suitability of selected candidates.

Finally, we will explore how Artificial Intelligence can optimize talent management within an organization, identifying and retaining key employees, personalizing career development paths, and performing competency analysis to detect skills gaps. In addition, the implementation of mentoring and virtual coaching programs, leadership potential assessments and change management strategies will be covered.

In this way, TECH has implemented a comprehensive university program, totally online, so that graduates will only need an electronic device with an Internet connection to access the teaching materials, avoiding problems such as traveling to a physical center and adjusting to a pre-established schedule. In addition, it includes the revolutionary Relearning methodology, consisting of the repetition of key concepts for optimal assimilation of the contents. 

You will be prepared to lead the digital transformation in HR, implementing innovative solutions that automate processes, eliminate biases in personnel selection and enhance employees' professional development”

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

• The development of case studies presented by experts in Artificial Intelligence focused on the HR Department
• The graphic, schematic, and practical contents with which they are created, provide 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 improve operational efficiency in personnel and payroll administration by automating crucial tasks, such as resource allocation and benefits management. What are you waiting for to enroll?"

The program includes in its teaching staff, professionals of the sector who pour into this specialization the experience of their work, in addition to recognized specialists from reference 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, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will become familiar with tools that will allow you to automate the analysis of resumes, filter and classify candidates, and conduct virtual interviews with the support of AI. With all TECH's quality guarantees!"

Bet on TECH! You will identify and retain key employees, customize career development paths, and apply AI to perform competency analysis and detect skills gaps"

This university program will train professionals in the automation of processes, such as personnel administration and payroll management, as well as in the advanced use of AI to improve personnel selection, eliminate biases and personalize professional development. In addition, skills will be acquired to improve the work climate through sentiment analysis and proactive detection of labor problems. Ethics, transparency and data protection will also be addressed, ensuring that graduates will not only master AI techniques, but will also understand the ethical and legal implications of its application in Human Resources.

The main objective of the university program will be to provide you with a comprehensive and specialized approach to the application of AI in all key areas of Human Resources”

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
• Develop an in-depth understanding of how Artificial Intelligence can be integrated into key Human Resources functions
• Enable students to use AI to automate and improve recruitment processes, from recruitment to final evaluation
• Apply AI to identify, retain and develop talent within the organization, personalizing employees' career growth
• Master the tools necessary to implement advanced performance appraisal systems using AI, with a focus on continuous assessment, real-time feedback and elimination of biases
• Use AI to monitor the work climate, proactively identifying problems and improving internal communication and employee satisfaction
• Develop the ability to use AI to identify and eliminate bias in selection, evaluation and development processes
• Train students to implement AI solutions that automate administrative and managerial tasks
• Apply predictive analytics techniques in HR management, anticipating needs and improving strategic planning
• Delve into the ethical and transparency principles necessary for the responsible implementation of AI in Human Resources
• Lead digital transformation projects in the Human Resources department, using AI as a key tool to innovate and improve organizational processes

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

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

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
• 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 Greedy algorithms, 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

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

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

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 Datasetsproject 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

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) 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.
• 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
• 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. Personnel and Payroll Management with AI

• Develop skills to implement AI solutions that automate personnel administration, payroll and resource allocation, improving personnel administration, payroll management, and resource allocation, improving operational efficiency
• Understand and apply AI technologies to ensure compliance with legal regulations in human resource management, minimizing legal risks

Module 17. Selection Processes and Artificial Intelligence

• Acquire skills to use AI in the automation of recruitment and selection tasks, from resume analysis to candidate evaluation
• Apply AI to identify and eliminate biases in the selection process, promoting fairer and more equitable practices

Module 18. AI and Its Application in Talent Management and Professional Development

• Develop the ability to use AI to customize employees' career development plans, tailoring growth to individual needs
• Apply AI to identify key talent within the organization and design effective retention strategies

Module 19. Performance Evaluations

• Train in the implementation of continuous evaluation systems that provide real-time feedback, improving the accuracy and relevance of performance evaluations
• Develop skills to use AI to analyze performance data, identifying patterns and areas for improvement

Module 20. Monitoring and Improving Work Climate with AI

• Use AI tools to analyze work climate through sentiment analysis, identifying problems and opportunities for improvement
• Develop the ability to apply AI to proactively detect and address workplace issues, improving internal communication and employee satisfaction

You will be trained to identify and eliminate biases in personnel selection, improve the work climate through sentiment analysis, and proactively address labor problems”