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

In order to optimize educational projects, teachers use AI tools to enrich the students' experience. However, to achieve the expected results, professionals need to have a broad knowledge of AI application strategies in the classroom. They will be able to develop resources such as chatbots, dynamic learning games and even tools to assess student performance.

In this context, TECH implements this program in Artificial Intelligence in Education , where the associated ethical, legal and social considerations will also be addressed. With an eminently practical approach, teachers will acquire tangible skills to implement AI procedures in the educational environment. Graduates will delve deeper into teaching praxis by focusing on actors such as personalization of learning and continuous improvement, which are indispensable for adaptability in the educational process. Finally, the syllabus will analyze in detail the emerging trends in AI for Education, ensuring that participants are aware of the latest innovations in educational technology.

It should be noted that this university program is based on a 100% online methodology so that students can learn at their own pace. To do so, the only thing they will need to access the resources is a device with Internet access. The academic itinerary stands out for relying on the innovative Relearning method. This is a teaching model supported by the repetition of the most important content, in order to make the knowledge last in the students' minds. To enrich learning, the materials are complemented by a wide variety of multimedia resources (such as interactive summaries, supplementary readings or infographics) to reinforce knowledge and skills. In this way, students will learn gradually and naturally, without having to resort to extra efforts such as memorization.

Want to facilitate instant feedback? With this university program you'll identify areas for improvement and offer personalized support’’

This Professional master’s degree in Artificial Intelligence in Education  contains the most complete and up-to-date educational program on the market. Its most notable features are:

• 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

Thanks to the revolutionary Relearning methodology, you will integrate all the knowledge in an optimal way to successfully achieve the results you are looking for’’

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will drive innovation and continuous improvement in education through the responsible use of technology"

You will have an advanced and exclusive program and will be able to face the challenges of the educational landscape driven by Machine Learning"

Composed of 20 modules, this university program stands out for its comprehensive and specialized approach. The syllabus goes beyond the technical aspects of AI in Education, delving into the associated ethical, legal and social considerations. In turn, the syllabus will provide students with state-of-the-art technological tools, so that their work as teachers integrates innovations such as Augmented Reality or Predictive Analytics. The training will also emphasize attention to the personalization of learning and continuous improvement, key aspects for adaptability in the educational process.

It includes clinical cases to bring the development of the program as close as possible to the reality of teaching care’’

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 Librtaries
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. Application of H2O.ai in the Methods of 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. Implementation of TensorFlow in the Interpretation of Educational Trends and Patterns using Machine Learning Techniques
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 Aulas 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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