AI in Education fosters adaptive, student-centered learning, promoting a more effective and enriching educational environment. Enroll now!”

The application of Artificial Intelligence (AI) in education has emerged as an invaluable tool, revolutionizing the way students access knowledge and how educators manage the teaching process. Personalization of learning has become more accessible thanks to intelligent algorithms, adapting educational content according to individual needs. This not only maximizes efficiency, but also addresses differences in learning pace and style. 

For this reason, TECH has developed this Artificial Intelligence in Education in Artificial Intelligence in Education, through which it will address not only the more technical aspects of AI, but also the associated ethical, legal and social considerations. In addition, the practical focus on the development of AI projects in the classroom will equip teachers with tangible skills for effective implementation in educational environments.  

In addition, the graduates will investigate teaching practice with generative AI, highlighting the focus on personalization of learning and continuous improvement, key aspects for adaptability in the educational process. Finally, emerging trends in AI for Education will be analyzed, ensuring that participants are aware of the latest innovations in educational technology. 

In this way, the program will provide a balanced combination of technical knowledge, practical skills and an ethical and reflective perspective, positioning itself as a leader in training professionals capable of addressing the challenges and opportunities of AI in education. 

TECH has devised a comprehensive program that is based on the Relearning methodology. This educational modality focuses on the repetition of essential concepts to ensure optimal understanding. Likewise, accessibility is key, since only an electronic device with an Internet connection is needed to access the contents at any time, eliminating the need to attend in person or adjust to pre-established schedules.

AI facilitates instant feedback, allowing teachers to identify areas for improvement and provide personalized support"

 

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

• The development of case studies presented by experts in Artificial Intelligence in Education
• The graphic, schematic and practical contents of the book provide theoretical 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 manage AI projects in classrooms, from programming with machine learning to use in video games and robotics"   

 

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

Through this 100% online program, you will integrate generative AI tools in the planning, implementation and evaluation of educational activities"

You will master the most cutting-edge AI technologies, such as Augmented/Virtual Reality, thanks to the extensive library of multimedia resources"

The program includes specific modules, such as "Ethics and Legislation of AI in Education" and "Teaching Practice with Generative AI", demonstrating its commitment to accountability and personalization of learning. In addition, the exploration of emerging trends in AI for Education will ensure that teachers are prepared to integrate the latest innovations, from Augmented Reality (AR) to predictive analytics, into their pedagogical practices. This combination of ethical foundations, practical application and incorporation of cutting-edge technologies will foster graduates' acquisition of the specific knowledge and skills to advance their professional careers. 

This Professional master’s degree combines technical aspects of Artificial Intelligence with a practical approach in the development of educational projects"

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 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 their 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. Criteria for Mathematical Analysis of 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. Layer Bonding 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. Learning Transfer 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. Learning Transfer 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. Learning Transfer 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. Loading and Preprocessing Data with TensorFlow 

10.5.1. Loading Data Sets with TensorFlow 
10.5.2. Preprocessing 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 Preprocessing Layers 

10.8.1. Using the Keras Preprocessing API 
10.8.2. Preprocessing Pipelined Construction with Keras 
10.8.3. Using the Keras Preprocessing API for Model Training 

10.9. The TensorFlow Datasets Project 

10.9.1. Using TensorFlow Datasets for Data Loading 
10.9.2. Preprocessing Data 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 Applications 
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. Learning by Transfer 
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 Applications 

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 the 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 the Health 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. Data Analysis and Application of AI Techniques for Educational Personalization 

16.1. Identification, Extraction and Preparation of Educational Data 

16.1.1. Applying H2O.ai in the Collection and Selection of Relevant Data in Educational Settings
16.1.2. Data Cleaning and Normalization Techniques for Educational Analyses 
16.1.3. Importance of Data Integrity and Quality in Educational Research 

16.2. Analysis and Evaluation of Educational Data with AI for Continuous Improvement in the Classroom 

16.2.1. Implementation of TensorFlow in the Interpretation of Educational Trends and Patterns using Machine Learning Techniques
16.2.2. Evaluating the Impact of Pedagogical Strategies using Data Analytics 
16.2.3. Application of Trinka in the Integration of AI-Based Feedback for the Optimization of the Teaching Process 

16.3. Definition of Academic Performance Indicators from Educational Data 

16.3.1. Establishment of Key Metrics for Evaluating Student Achievement 
16.3.2. Comparative Analysis of Indicators to Identify Areas for Improvement 
16.3.3. Correlation Between Academic Indicators and External Factors Using AI 

16.4. AI Tools for Educational Decision Making and Monitoring 

16.4.1. Decision Support Systems Based on tome.ai for Educational Administrators 
16.4.2. Use of Trello for Educational Resource Planning and Allocation
16.4.3. Optimization of Educational Processes through Predictive Analytics with Orange Data Mining  

16.5. AI Technologies and Algorithms for Predictive Analysis of Academic Achievement Data 

16.5.1. Fundamentals of Predictive Modeling in Education 
16.5.2. Use of Classification and Regression Algorithms to Predict Trends in Education 
16.5.3. Case Studies of Successful Predictions in Educational Environments 

16.6. Application of Data Analytics with AI for the Prevention and Solution of Educational Problems 

16.6.1. Early Identification of Academic Risks through Predictive Analytics 
16.6.2. Data-driven Intervention Strategies to Address Educational Challenges 
16.6.3. Evaluating the Impact of DataRobot AI-Based Solutions in Education

16.7. Personalized Diagnosis of Learning Difficulties from Data Analytics with AI 

16.7.1. AI Techniques for the Identification of Learning Styles and Learning Difficulties with IBM Watson Education 
16.7.2. Integration of Data Analysis into Individualized Educational Support Plans 
16.7.3. Case Studies of Diagnoses Improved by the Use of AI 

16.8. Data Analysis and Application of AI for Identification of Special Educational Needs 

16.8.1. AI Approaches to Special Educational Needs Screening with Gooroo 
16.8.2. Personalization of Teaching Strategies Based on Data Analysis 
16.8.3. Evaluation of the Impact of AI on Educational Inclusion 

16.9. Personalization of Learning with AI from Academic Performance Data Analytics 

16.9.1. Creating Adaptive Learning Pathways using Smart Sparrow 
16.9.2. Implementation of Recommender Systems for Educational Resources 
16.9.3. Real-Time Individual Progress Measurement and Adjustment via Squirrel AI Learning 

16.10. Security and Privacy in the Processing of Educational Data 

16.10.1. Ethical and Legal Principles in the Management of Educational Data 
16.10.2. Data Protection and Privacy Techniques for Educational Systems with Google Cloud Security 
16.10.3. Case Studies on Security Breaches and their Impact on Education 

Module 17. Development of Artificial Intelligence Projects in the Classroom    

17.1. Planning and Design of AI Projects in Education with Algor Education

17.1.1. First Steps to Plan the Project  
17.1.2. Knowledge Bases  
17.1.3. Design of AI Projects in Education   

17.2. Tools for the Development of Educational Projects with AI  

17.2.1. Tools for Developing Educational Projects: TensorFlow Playground
17.2.2. Tools for Educational Projects in History  
17.2.3. Tools for Educational Projects in Mathematics; Wolfram Alpha
17.2.4. Tools for Educational Projects in English: Grammarly

17.3. Strategies for Implementing AI Projects in the Classroom  

17.3.1. When to Implement an AI Project  
17.3.2. Why Implement an AI Project  
17.3.3. Strategies to Be Implemented   

17.4. Integration of AI Projects in Specific Subjects  

17.4.1. Mathematics and AI: Thinkster Math
17.4.2. History and AI  
17.4.3. Languages and AI: Deep L
17.4.4. Other Subjects: Watson Studio

17.5. Project 1: Developing Educational Projects Using Machine Learning with Khan Academy

17.5.1. First Steps  
17.5.2. Requirements  
17.5.3. Tools to Be Used  
17.5.4. Project Definition    

17.6. Project 2: Integration of AI in the Development of Educational Games   

17.6.1. First Steps  
17.6.2. Requirements  
17.6.3. Tools to Be Used  
17.6.4. Project Definition    

17.7. Project 3: Development of Educational Chatbots for Student Assistance 

17.7.1. First Steps  
17.7.2. Requirements  
17.7.3. Tools to Be Used  
17.7.4. Project Definition   

17.8. Project 4: Integrating Intelligent Agents into Educational Platforms with Knewton

17.8.1. First Steps  
17.8.2. Requirements  
17.8.3. Tools to Be Used  
17.8.4. Project Definition   

17.9. Evaluating and Measuring the Impact of AI Projects in Education with Qualtrics

17.9.1. Benefits of Working with AI in the Classroom  
17.9.2. Actual Data  
17.9.3. AI in the Classroom   
17.9.4. AI Statistics in Education   

17.10. Analysis and Continuous Improvement of AI in Education with Edmodo Insights

17.10.1. Current Projects   
17.10.2. Commissioning  
17.10.3. What the Future Holds  
17.10.4. Transforming the Classroom 360  

Module 18. Teaching Practice with Generative Artificial Intelligence    

18.1. Generative AI Technologies for Use in Education  

18.1.1. Current Market: Artbreeder, Runway ML and DeepDream Generator
18.1.2. Technologies in Use  
18.1.3. What is to Come  
18.1.4. The Future of the Classroom  

18.2. Application of Generative AI Tools in Educational Planning  

18.2.1. Planning Tools: Altitude Learning
18.2.2. Tools and Their Application  
18.2.3. Education and AI  
18.2.4. Evolution   

18.3. Creating Teaching Materials with Generative AI Using Story Ai, Pix2PIx and NeouralTalk2

18.3.1. AI and its Uses in the Classroom  
18.3.2. Tools to Create Teaching Material  
18.3.3. How to Work with the Tools  
18.3.4. Commands  

18.4. Development of Evaluation Tests using Generative AI with Quizgecko

18.4.1. AI and its Uses in the Development of Evaluation Tests   
18.4.2. Tools for the Development of Evaluation Tests   
18.4.3. How to Work with the Tools  
18.4.4. Commands   

18.5. Enhanced Feedback and Communication with Generative AI  

18.5.1. AI in Communication  
18.5.2. Application of Tools in the Development of Communication in the Classroom  
18.5.3. Advantages and Disadvantages   

18.6. Correction of Activities and Evaluative Tests using Generative AI with Gradescope AI

18.6.1. AI and its Uses in the Correction of Evaluative Activities and Tests  
18.6.2. Tools for the Correction of Evaluative Activities and Tests   
18.6.3. How to Work with the Tools  
18.6.4. Commands  

18.7. Generation of Teacher Quality Assessment Surveys through Generative AI  

18.7.1. AI and its Uses in the Generation of Teaching Quality Assessment Surveys using AI   
18.7.2. Tools for the Generation of AI-based Teacher Quality Surveys  
18.7.3. How to Work with the Tools  
18.7.4. Commands  

18.8. Integration of Generative AI Tools in Pedagogical Strategies  

18.8.1. Applications of AI in Pedagogical Strategies  
18.8.2. Correct Uses   
18.8.3. Advantages and Disadvantages  
18.8.4. Generative AI Tools in Pedagogical Strategies: Gans

18.9. Use of Generative AI for Universal Design for Learning  

18.9.1. Generative AI, Why Now?  
18.9.2. AI in Learning  
18.9.3. Advantages and Disadvantages  
18.9.4. Applications of AI in Learning  

18.10. Evaluating the Effectiveness of Generative AI in Education  

18.10.1. Effectiveness Data  
18.10.2. Projects  
18.10.3. Design Purposes  
18.10.4. Evaluating the Effectiveness of AI in Education   

Module 19. Innovations and Emerging Trends in AI for Education 

19.1. Emerging AI Tools and Technologies in Education  

19.1.1. Obsolete AI Tools  
19.1.2. Current Tools: ClassDojo and Seesaw
19.1.3. Future Tools   

19.2. Augmented and Virtual Reality in Education  

19.2.1. Augmented Reality Tools  
19.2.2. Virtual Reality Tools  
19.2.3. Application of Tools and their Uses  
19.2.4. Advantages and Disadvantages  

19.3. Conversational AI for Educational Support and Interactive Learning with Wysdom AI and SnatchBot

19.3.1. Conversational AI, Why Now?  
19.3.2. AI in Learning  
19.3.3. Advantages and Disadvantages  
19.3.4. Applications of AI in Learning  

19.4. Application of AI for Improving Knowledge Retention  

19.4.1. AI as a Support Tool  
19.4.2. Guidelines to Follow   
19.4.3. AI Performance in Knowledge Retention  
19.4.4. AI and Support Tools  

19.5. Facial and Emotional Recognition Technologies for Tracking Learner Engagement and Well-Being   

19.5.1. Facial and Emotional Recognition Technologies on the Market Today  
19.5.2. Uses  
19.5.3. Applications  
19.5.4. Margin of Error  
19.5.5. Advantages and Disadvantages  

19.6. Blockchain and AI in Education to Transform Educational Administration and Certification   

19.6.1. What Is Blockchain?  
19.6.2. Blockchain and Its Applications  
19.6.3. Blockchain as a Transformative Element  
19.6.4. Educational Administration and Blockchain  

19.7. Emerging AI Tools to Enhance the Learning Experience with Squirrel AI Learning

19.7.1. Current Projects   
19.7.2. Commissioning  
19.7.3. What the Future Holds  
19.7.4. Transforming the Classroom 360  

19.8. Strategies for Developing Pilots with Emerging AI  

19.8.1. Advantages and Disadvantages  
19.8.2. Strategies to be Developed  
19.8.3. Key Points  
19.8.4. Pilot Projects  

19.9. Analysis of Successful AI Innovation Cases  

19.9.1. Innovative Projects  
19.9.2. Application of AI and its Benefits  
19.9.3. AI in the Classroom, Successful Cases  

19.10. Future of AI in Education  

19.10.1. AI History in Education  
19.10.2. Where is AI going in the Classroom?  
19.10.3. Future Projects  

Module 20. Ethics and Legislation of Artificial Intelligence in Education 

20.1. Identification and Ethical Treatment of Sensitive Data in the Educational Context 

20.1.1. Principles and Practices for the Ethical Handling of Sensitive Data in Education 
20.1.2. Challenges in Protecting the Privacy and Confidentiality of Student Data 
20.1.3. Strategies for Ensuring Transparency and Informed Consent in Data Collection 

20.2. Social and Cultural Impact of AI in Education 

20.2.1. Analysis of the Effect of AI on Social and Cultural Dynamics in Educational Environments 
20.2.2. Exploring How Microsoft AI for Accessibility Can Perpetuate or Mitigate Social Biases and Inequalities 
20.2.3. Assessing the Social Responsibility of Developers and Educators in the implementation of AI 

20.3. AI Legislation and Data Policy in Educational Settings 

20.3.1. Review of Current Data and Privacy Laws and Regulations Applicable to AI in Education 
20.3.2. Impact of Data Policies on Educational Practice and Technological Innovation 
20.3.3. Developing Institutional Policies for the Ethical Use of AI in Education with AI Ethics Lab 

20.4. Assessing the Ethical Impact of AI 

20.4.1. Methods for Assessing the Ethical Implications of AI Applications in Education 
20.4.2. Challenges in Measuring the Social and Ethical Impact of AI 
20.4.3. Creating Ethical Frameworks to Guide the Development and Use of AI in Education 

20.5. Challenges and Opportunities of AI in Education 

20.5.1. Identification of Major Ethical and Legal Challenges in the Use of AI in Education 
20.5.2. Exploring Opportunities to Improve Teaching and Learning through Squirrel AI Learning 
20.5.3. Balancing Technological Innovation and Ethical Considerations in Education 

20.6. Ethical Application of AI Solutions in the Educational Environment 

20.6.1. Principles for Ethical Design and Deployment of AI Solutions in Education 
20.6.2. Case Studies on Ethical Applications of AI in Different Educational Contexts 
20.6.3. Strategies for Involving All Stakeholders in Ethical AI Decision-Making 

20.7. AI, Cultural Diversity and Gender Equity 

20.7.1. Analysis of the Impact of AI on the Promotion of Cultural Diversity and Gender Equity in Education 
20.7.2. Strategies for Developing Inclusive and Diversity-Sensitive AI Systems with Teachable Machine by Google 
20.7.3. Assessment of how AI can Influence the Representation and Treatment of Different Cultural and Gender Groups 

20.8. Ethical Considerations for the use of AI Tools in Education 

20.8.1. Ethical Guidelines for the Development and Use of AI Tools in the Classroom 
20.8.2. Discussion on the Balance between Automation and Human Intervention in Education 
20.8.3. Analysis of Cases where the use of AI in Education has Raised Significant Ethical Issues 

20.9. Impact of AI on Educational Accessibility 

20.9.1. Exploration of how AI can Enhance or Limit Accessibility in Education 
20.9.2. Analysis of AI Solutions Designed to Increase Inclusion and Access to Education for All with Google Read Along  
20.9.3. Ethical Challenges in Implementing AI Technologies to Improve Accessibility 

20.10. Global Case Studies in AI and Education 

20.10.1. Analysis of International Case Studies on the Use of AI in Education 
20.10.2. Comparison of Ethical and Legal Approaches in Different Educational Cultural Contexts 
20.10.3. Lessons Learned and Best Practices from Global Cases in AI and Education. 

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