This program will allow you to master all the fundamental aspects involved in managing any type of business project, and will prepare you to lead your company to immediate success"

Why study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class centre for intensive managerial skills training.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"     

At TECH Global University

Innovation

The university offers an online learning model that combines the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus. 

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system.  

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high... 

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.

100,000+ executives trained each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.    

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

Show the world your talent after completing this program. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH students represent more than 200 different nationalities. 

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique: 

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.    

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (a postgraduate learning methodology with the highest international rating) with the Case Study. A complex balance between tradition and state-of-the-art, within the context of the most demanding academic itinerary.  

Economy of Scale

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case studies in the academic community”

The program will cover a wide range of essential topics, from the automation of personnel and payroll administration to the optimization of selection processes through artificial intelligence. Employers will learn about predictive analytics for talent management and the personalization of professional development, advanced techniques for evaluating performance and improving the work environment. In addition, they will explore the practical application of emerging technologies in recruitment and the elimination of biases, providing concrete tools to implement effective, data-driven solutions. 

The content of the Professional master’s degree has been carefully designed to address the strategic and operational needs of HR departments in the digital era” 

Syllabus

The curriculum has been designed to equip professionals with the necessary skills to revolutionize personnel administration by integrating advanced technologies. They will be able to optimize payroll administration and personnel management using Artificial Intelligence. In this sense, they will be able to automate critical processes, ensure regulatory compliance and improve resource allocation. In addition, the application of AI in recruitment and selection processes will be addressed, using tools and techniques to automate the evaluation of resumes, conduct AI-assisted virtual interviews and eliminate biases in candidate selection. 

It will also focus on talent management and professional development through the use of AI, so that employers are able to identify and retain key talent, customize development plans and use predictive analytics to manage competencies and skills gaps. It will also analyze how AI can support mentoring and virtual coaching, as well as facilitate the assessment of leadership potential and organizational change management. 

In this way, TECH has created a comprehensive university program, in a fully online format, allowing graduates to access educational materials from any device with an Internet connection. This eliminates the need to travel to a physical location and adapt to predetermined schedules. In addition, it uses the revolutionary Relearning methodology, which focuses on the repetition of key concepts to ensure a complete understanding of the content. 

This Professional master’s degree takes place over 12 months and is divided into 20 modules:

Module 1. Fundamentals of Artificial Intelligence
Module 2. Data Types and Data Life Cycle 
Module 3. Data in Artificial Intelligence 
Module 4. Data Mining: Selection, Pre-Processing and Transformation
Module 5. Algorithm and Complexity in Artificial Intelligence  
Module 6. Intelligent Systems  
Module 7. Machine Learning and Data Mining 
Module 8. Neural Networks, the Basis of Deep Learning 
Module 9. Deep Neural Networks Training  
Module 10. Model Customization and Training with TensorFlow
Module 11. Deep Computer Vision with Convolutional Neural Networks 
Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention  
Module 13. Autoencoders, GANs, and Diffusion Models 
Module 14. Bio-Inspired Computing   
Module 15. Artificial Intelligence: Strategies and Applications 
Module 16. Personnel and Payroll Management with AI
Module 17. Selection Processes and Artificial Intelligence
Module 18. AI and Its Application in Talent Management and Professional Development 
Module 19. Performance Evaluations 
Module 20. Monitoring and Improving Work Climate with AI

Where, When and How is it Taught?

TECH offers the possibility to develop this Professional master’s degree in Artificial Intelligence in Human Resources completely online. Throughout the 12 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Film
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation: Semantic Web

1.6. Semantic Web

1.6.1. Specifications RDF, RDFS and OWL
1.6.2. Inference/ Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, Whatsapp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Data Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to Its Shape

2.2.2.1. Numeric
2.2.2.2. Text:
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
2.7.3. Data Mining

2.8. Datawarehouse

2.8.1. Elements that Comprise It
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory Aspects

Module 3. Data in Artificial Intelligence

3.1. Data Science
3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model
3.10.3. Useful Tools

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

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data
4.3.3. Transforming Attributes

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data Vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Pre-processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Mathematical Analysis Criteria for Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Merge Sort
5.3.6. Quick Sort

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology

6.6. Ontology Languages and Ontology Creation Software

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Pre-processing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. Algorithm C
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10 Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Hidden Layer
8.3.3. Output Layer

8.4. Union of Layers and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the First Neural Network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Transfer Learning Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Transfer Learning Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Transfer Learning Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Grap Optimization with TensorFlow Operations

10.5. Data Loading and Pre-Processing with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Pre-Processing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction with Keras
10.8.3. Using the Keras Pre-Processing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Data Pre-Processing with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Application
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Use of the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet- using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-trained Keras Models

11.6.1. Characteristics of Pre-Trained Models
11.6.2. Uses of Pre-Trained Models
11.6.3. Advantages of Pre-Trained Models

11.7. Pre-Trained Models for Transfer Learning

11.7.1. Transfer Learning
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.1. Edge Detection
11.10.1. Rule-Based Segmentation Methods

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

12.1. Text Generation using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Using Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Preprocessing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Application

12.10.1. Development of a Natural Language Processing Application with RNN and Attention
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Application
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing 

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications 

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services  Opportunities and Challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/Uses of AI

15.2. Implications of Artificial Intelligence in Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector Opportunities and Challenges
15.2.2. Case Uses

15.3. Risks Related to the Use of AI in Healthcare Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/Uses of AI

15.4. Retail

15.4.1. Implications of AI in Retail. Opportunities and Challenges
15.4.2. Case Uses
15.4.3. Potential Risks Related to the Use of AI
15.4.4. Potential Future Developments/Uses of AI

15.5. Industry

15.5.1. Implications of AI in Industry Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential Risks Related to the Use of AI in Industry

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/Uses of AI

15.7. Public Administration

15.7.1. AI Implications for Public Administration Opportunities and Challenges
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Educational

15.8.1. AI Implications for Education Opportunities and Challenges
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/Uses of AI

15.9. Forestry and Agriculture

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Uses
15.9.3. Potential Risks Related to the Use of AI
15.9.4. Potential Future Developments/Uses of AI

15.10. Human Resources

15.10.1. Implications of AI for Human Resources Opportunities and Challenges
15.10.2. Case Uses
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/Uses of AI

Module 16. Personnel and Payroll Management with AI

16.1. Artificial Intelligence for Diversity and Inclusion in the Workplace

16.1.1. Diversity Analysis Using IBM Watson to Detect Trends and Biases
16.1.2. AI Tools for Detecting and Correcting Biases in HR Processes
16.1.3. Evaluating the Impact of Inclusion Policies using Data Analytics

16.2. Fundamentals of Personnel Administration with AI

16.2.1. Automation of Hiring and Onboarding Processes
16.2.2. Use of AI-Based Personnel Data Management Systems
16.2.3. Improving the Employee Experience through Intelligent Platforms

16.3. AI Technologies Applied to Payroll

16.3.1. AI Systems for Automated Payroll Calculation
16.3.2. Intelligent Profit Management with Platforms such as Gusto
16.3.3. Detection of Errors and Fraud in Payrolls Using AI Algorithms

16.4. Optimizing Resource Allocation with AI

16.4.1. Personnel Planning with Kronos Predictive Tools
16.4.2. AI Models for Shift and Task Assignment Optimization
16.4.3. Workload Analysis and Resource Allocation with Power BI

16.5. AI in HR Regulatory and Legal Compliance

16.5.1. Automation of Compliance with Labor Policies
16.5.2. AI Systems to Ensure Fairness and Transparency in HR
16.5.3. Contract and Regulatory Management with IBM Watson Legal Advisor

16.6. Predictive Analytics in Workforce Management

16.6.1. Predictive Models for Employee Retention with Retain's AI
16.6.2. Sentiment Analysis in Internal Communications
16.6.3. Predicting Training and Development Needs

16.7. Automating Benefits Management with AI

16.7.1. Benefits Administration Using Intelligent Platforms such as Zenefits
16.7.2. Customizing Benefit Packages using AI
16.7.3. Optimizing Benefit Costs Using Data Analytics

16.8. Integrating HR Systems with AI

16.8.1. Integrated Systems for Personnel Management with Salesforce Einstein
16.8.2. Interface and Usability in AI-Based HR Systems
16.8.3. Data Security and Privacy in Integrated Systems

16.9. AI-Supported Training and Development of Personnel

16.9.1. Adaptive and Personalized Learning Systems
16.9.2. AI-Powered E-Learning Platforms
16.9.3. Performance Assessment and Monitoring Using Intelligent Technologies

16.10. Crisis and Change Management with AI in HR

16.10.1. Using AI for Effective Management of Organizational Change
16.10.2. Predictive Tools for Crisis Preparedness with Predictive Layer
16.10.3. Data Analysis to Evaluate and Adapt HR Strategies in Times of Crisis

Module 17. Artificial Intelligence: Strategies and Applications 

17.1. Introduction to the Application of Artificial Intelligence in Personnel Selection

17.1.1. Definition of Artificial Intelligence in the Human Resources Context. Entelo
17.1.2. Importance of Applying AI in Selection Processes
17.1.3. Benefits of Using AI in Selection Processes

17.2. Automating Tasks in the Recruitment Process

17.2.1. Using AI to Automate Job Postings
17.2.2. Implementing Chatbots to Answer Candidates' Frequently Asked Questions
17.2.3. Tools XOR

17.3. Resume Analysis with AI

17.3.1. Using AI Algorithms to Analyze and Evaluate Resumes. Talview
17.3.2. Automatic Identification of Skills and Experience Relevant to the Position
17.3.3. Advantages and Disadvantages

17.4. Candidate Filtering and Ranking

17.4.1. Applying AI to Automatically Filter Candidates Based on Specific Criteria. Vervoe
17.4.2. Ranking Candidates According to Suitability for the Position Using Machine Learning Techniques
17.4.3. Using AI for Dynamic Customization of Filtering Criteria based on Job Needs

17.5. Pattern Recognition on Social Networks and Professional Platforms

17.5.1. Using AI to Analyze Candidate Profiles on Social Networks and Professional Platforms
17.5.2. Identifying Behavioral Patterns and Trends Relevant to Recruiting
17.5.3. Assessing the Online Presence and Digital Influence of Candidates Using AI Tools

17.6. AI-Assisted Virtual Interviewing

17.6.1. Implementing Virtual Interviewing Systems with Language and Emotion Analysis. Talentoday
17.6.2. Automatic Evaluation of Candidate Responses Using Natural Language Processing Techniques
17.6.3. Developing Automatic and Personalized Feedback for Candidates Based on AI Interview Analysis

17.7. Evaluation of Skills and Competencies

17.7.1. Using AI-Based Assessment Tools to Measure Technical and Soft Skills. OutMatch
17.7.2. Automatically Analyzing Tests and Assessment Exercises Performed by Candidates. Harver
17.7.3. Correlation of Assessment Results with Success on the Job Using AI Predictive Analytics

17.8. Elimination of Selection Biases

17.8.1. Applying AI to Identify and Mitigate Unconscious Bias in the Selection Process
17.8.2. Implementing Unbiased and Fair AI Algorithms in Decision Making
17.8.3. Training and Continuous Tuning of AI Models to Ensure Fairness in Personnel Selection

17.9. Prediction of Fit and Retention

17.9.1. Using Predictive AI Models to Predict Candidate Suitability and Likelihood of Retention Hiretual
17.9.2. Analyzing Historical Data and Performance Metrics to Identify Patterns of Success
17.9.3. AI Models for Simulating Job Scenarios and Their Impact on Candidate Retention

17.10. Ethics and Transparency in AI Selection

17.10.1. Ethical Considerations in the Use of AI in the Personnel Selection Processes
17.10.2. Ensuring Transparency and Explainability in AI Algorithms Used in Hiring Decisions
17.10.3. Developing Audit and Review Policies for Automated Decisions

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

18.1. Introduction to the Application of AI in Talent Management and Professional Development

18.1.1. Historical Evolution of AI in Talent Management and How It Has Transformed the Industry
18.1.2. Definition of Artificial Intelligence in the Human Resources Context
18.1.3. Importance of Talent Management and Professional Development. Glint

18.2. Automation of Talent Management Processes

18.2.1. Using AI to Automate Administrative Tasks in Talent Management
18.2.2. Implementing AI-Based Talent Management Systems
18.2.3. Assessing Operational Efficiency and Cost Reduction through Automation with AI

18.3. Talent Identification and Retention with AI

18.3.1. Using AI Algorithms to Identify and Retain Talent in the Organization
18.3.2. Predictive Analytics for the Detection of Employees with High Growth Potential
18.3.3. Integrating AI with HR Management Systems for Continuous Performance and Development Tracking

18.4. Personalization of Professional Development. Leader Amp

18.4.1. Implementing Customized AI-Based Professional Development Programs
18.4.2. Using Recommendation Algorithms to Suggest Learning and Growth Opportunities
18.4.3. Matching Career Development Pathways to Labor Market Evolution Predictions Using AI

18.5. Competency and Skill Gap Analysis

18.5.1 Using AI to Analyze Employees' Current Skills and Competencies
18.5.2. Identification of Skills Gaps and Training Needs Using Data Analytics
18.5.3. Implementing Real-Time Training Programs Based on Automated AI Recommendations

18.6. Mentoring and Virtual Coaching

18.6.1. Implementation of AI-Assisted Virtual Mentoring Systems. Crystal
18.6.2. Using Chatbots and Virtual Assistants to Provide Personalized Coaching
18.6.3. Impact Assessment of Virtual Coaching Using Data Analysis and Automated AI Feedback

18.7. Achievement and Performance Recognition

18.7.1. Using AI-Based Achievement Recognition Systems to Motivate Employees BetterUp
18.7.2. Automatically Analyzing Employee Performance and Productivity Using AI
18.7.3. Developing an AI-Based Reward and Recognition System

18.8. Evaluation of Leadership Potential

18.8.1. Applying AI Techniques to Assess Leadership Potential of Employees
18.8.2. Identifying Emerging Leaders and Developing Tailored Leadership Programs
18.8.3. Using AI-Driven Simulations to Train and Evaluate Leadership Skills

18.9. Change Management and Organizational Adaptability

18.9.1. Predictive Analytics to Anticipate Change Needs and Promote Organizational Resilience
18.9.2. Organizational Change Planning Using AI
18.9.3. Using AI to Manage Organizational Change and Promote Adaptability Cognician

18.10. Ethics and Accountability in Talent Management with AI

18.10.1. Ethical Considerations in the Use of AI in Talent Management and Professional Development. Reflektive
18.10.2. Ensuring Fairness and Transparency in AI Algorithms Used in Talent Management Decision-Making
18.10.3. Implementation of Audits to Monitor and Adjust AI Algorithms to Ensure Ethical Practices

Module 19. Performance Evaluations

19.1. Introduction to the Application of AI in Performance Appraisals

19.1.1. Definition of Artificial Intelligence and Its Role in Performance Appraisals. 15Five
19.1.2. Importance of Using AI to Improve the Objectivity and Efficiency of Appraisals
19.1.3. Limitations of AI in Performance Appraisals

19.2. Automation of Evaluation Processes

19.2.1. Using AI to Automate Data Collection and Analysis in Performance Appraisals Peakon
19.2.2. Implementing AI-Based Automated Evaluation Systems
19.2.3. Successful Studies in Automation with AI

19.3. Data Analysis and Performance Metrics

19.3.1. Using AI Algorithms to Analyze Performance Data and Trends
19.3.2. Identifying Key Metrics and KPIs Using Advanced Data Analysis Techniques
19.3.3. AI Data Analytics Training

19.4. Continuous Evaluation and Real-Time Feedback

19.4.1. Implementing AI-Assisted Continuous Assessment Systems. Lattice
19.4.2. Using Chatbots and Real-Time Feedback Tools to Provide Feedback to Employees
19.4.3. Impact of AI-Based Feedback

19.5. Identification of Strengths and Areas for Improvement

19.5.1. Applying AI to Identify Employee Strengths and Weaknesses
19.5.2. Automatic Analysis of Competencies and Skills Using Machine Learning Techniques. Workday Performance Management
19.5.3. Connection with Professional Development and Planning

19.6. Detection of Trends and Performance Patterns

19.6.1. Using AI to Detect Trends and Patterns in Employee Performance. TAlentSoft
19.6.2. Predictive Analytics to Anticipate Potential Performance Problems and Take Proactive Measures
19.6.3. Advanced Data Visualization Dashboards

19.7. Customization of Objectives and Development Plans

19.7.1. Implementing AI-Based Personalized Target Setting Systems. Reflektive
19.7.2. Using Recommendation Algorithms to Suggest Individualized Development Plans
19.7.3. Long-Term Impact of Personalized Targets

19.8. Elimination of Bias in Evaluations

19.8.1. Applying AI to Identify and Mitigate Bias in Performance Appraisals
19.8.2. Implementing Impartial and Equitable Algorithms in Evaluation Processes
19.8.3. AI Ethics Training for Evaluators

19.9.  Data Security and Protection in AI Evaluations

19.9.1. Ethical and Legal Considerations in the Use of Personal Data in Performance Evaluations with AI. LEver
19.9.2. Ensuring the Privacy and Security of Employee Information in AI-Based Evaluation Systems
19.9.3. Implementing Data Access Protocols

19.10. Continuous Improvement and Adaptability of the System

19.10.1. Using Feedback and Data Analysis to Continuously Improve Evaluation Processes
19.10.2. Adapting Evaluation Systems as the Organization's Needs and Objectives Change
19.10.3. Review Committee for Adjustment of Metrics

Module 20. Monitoring and Improving Work Climate with AI

20.1. Applying AI in Workplace Climate Management

20.1.1. Definition and Relevance of Work Climate
20.1.2. Overview of AI in the Management of Workplace Climate
20.1.3. Benefits of Using AI to Monitor Workplace Climate

20.2. AI Tools for Workplace Data Collection

20.2.1. Real-Time Feedback Systems with IBM Watson
20.2.2. Automated Survey Platforms
20.2.3. Sensors and Wearables for Physical and Environmental Data Collection

20.3. Sentiment Analysis with AI

20.3.1. Fundamentals of Sentiment Analysis
20.3.2. Using Google Cloud Natural Language to Analyze Emotions in Written Communication
20.3.3. Applying Sentiment Analysis in Emails and Corporate Social Networks

20.4. Machine Learning for the Identification of Behavioral Patterns

20.4.1. Clustering with K-Means in Python for Segmenting Workplace Behaviors
20.4.2. Pattern Recognition in Behavioral Data
20.4.3. Predicting Trends in Work Climate

20.5. AI in the Proactive Detection of Workplace Problems

20.5.1. Predictive Models to Identify Conflict Risks
20.5.2. AI-Based Early Warning Systems
20.5.3. Detection of Harassment and Discrimination Using Text Analytics with spaCy

20.6. Improving Internal Communication with AI

20.6.1. Chatbots for Internal Communication
20.6.2. Network Analysis with AI to Improve Collaboration Using Gephi
20.6.3. AI Tools to Personalize Internal Communications

20.7. Change Management with AI Support

20.7.1. AI Simulations to Predict Impacts of Organizational Change with AnyLogic
20.7.2. AI Tools to Manage Resistance to Change
20.7.3. AI Models for Optimizing Change Strategies

20.8. Assessment and Continuous Improvement of Work Climate with AI

20.8.1. Continuous Work Climate Monitoring Systems
20.8.2. Algorithms for Analyzing the Effectiveness of Interventions
20.8.3. AI for the Customization of Work Climate Improvement Plans

20.9. Integration of AI and Organizational Psychology

20.9.1. Psychological Theories Applied to AI Analysis
20.9.2. AI Models for Understanding Motivation and Job Satisfaction
20.9.3. AI Tools to Support Employee Emotional Well-Being

20.10. Ethics and Privacy in the Use of AI to Monitor Workplace Climate

20.10.1. Ethical Considerations of Workplace Monitoring
20.10.2. Data Privacy and Regulatory Compliance
20.10.3. Transparent and Responsible Data Management