Thanks to this 100% online program, you will get the most out of Big Data and analyze trends that influence the performance of financial assets” 

According to a study conducted by the International Finance Association, 70% of the entities that implement Artificial Intelligence solutions have managed to improve the accuracy of their economic analysis and optimize the management of their portfolios. Faced with this reality, more and more companies are demanding the incorporation of professionals who skillfully handle emerging tools such as Big Data, Natural Language Processing or Convolutional Neural Networks to make more informed strategic decisions and improve financial risk management. To take advantage of these job opportunities, experts need to have a competitive advantage that differentiates them from other candidates.

With this in mind, TECH is launching a revolutionary program in Artificial Intelligence in the Financial Department. Devised by renowned experts in this field, the academic itinerary will provide professionals with advanced skills to handle advanced tools ranging from Data Mining or Deep Computer Vision to Recurrent Neural Network models. Therefore, graduates will be highly qualified to use predictive models in financial risk management, optimize tedious tasks such as treasury management and even automate other processes such as internal audits. In addition, the didactic materials will delve into the most innovative methods for optimizing various investment portfolios. Also, the syllabus will offer advanced tools for designing complex economic data visualizations using Google Data Studio.

Moreover, the course is based on the revolutionary Relearning methodology promoted by TECH. This is a learning system that consists of the progressive reiteration of key aspects, which ensures that the essential concepts of the syllabus remain in the minds of the graduates. In addition, the syllabus can be planned individually, as there are no preset schedules or evaluation chronograms. Along the same lines, the Virtual Campus will be available 24 hours a day and will allow professionals to download the materials and consult them whenever they wish.

You will reach your full potential in the field of Financial Management with the help of multimedia resources in formats such as interactive summaries, explanatory videos and specialized readings”

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

• The development of case studies presented by experts in Artificial Engineering
• The graphic, schematic and practical contents with which it is conceived provide complete and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out 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

Looking to incorporate the most innovative Natural Language Processing techniques into your daily practice? Get it with this university program in less than a year”

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 efficiently train Machine Learning models, which will allow you to foresee various potential financial risks"

You will have access to a learning system based on repetition, with natural and progressive teaching throughout the entire syllabus"

Through this program, professionals will handle the main tools of Artificial Intelligence to optimize financial processes and improve strategic decision making.  The syllabus will delve into aspects such as the life cycle of data, algorithms and the specialization of Deep Neural Networks. Therefore, graduates will acquire skills to use predictive models to manage financial risks, improve planning in tasks such as treasury management and automate auditing tasks. Likewise, the syllabus will offer modern techniques to optimize investment portfolios and visualize complex economic data through Google Data Studio.

You will design automation solutions that increase efficiency in key tasks such as accounting, treasury management and internal auditing”

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. Cloak
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. 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 Tfdata API for Data Processing
10.6.2. Construction of Data Streams with Tfdata
10.6.3. Using the Tfdata 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 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
11.10.2. Edge Detection
11.10.3. 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 the 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 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. Automation of Financial Department Processes with Artificial Intelligence

16.1. Automation of Financial Processes with Artificial Intelligence and Robotic Process Automation (RPA)

16.1.1. AI and RPA for Process Automation and Robotization 
16.1.2. RPA Platforms for Financial Processes: UiPath, Blue Prism, and Automation Anywhere
16.1.3. Evaluation of RPA Use Cases in Finance and Expected ROI

16.2. Automated Invoice Processing with AI with Kofax

16.2.1. Configuration of AI Solutions for Invoice Processing with Kofax 
16.2.2. Application of Machine Learning Techniques for Invoice Classification
16.2.3. Automation of the Accounts Payable Cycle with AI Technologies

16.3. Payment Automation with AI Platforms

16.3.1. Implementing Automated Payment Systems with Stripe Radar and AI
16.3.2. Use of Predictive AI Models for Efficient Cash Management
16.3.3. Security in Automated Payment Systems: Fraud Prevention with AI

16.4. Bank Reconciliation with AI and Machine Learning

16.4.1. Automation of Bank Reconciliation Using AI with Platforms Such as Xero
16.4.2. Implementation of Machine Learning Algorithms to Improve Accuracy
16.4.3. Case Studies: Efficiency Improvements and Error Reduction

16.5. Cash Flow Management with Deep Learning and TensorFlow

16.5.1. Predictive Cash Flow Modeling with LSTM Networks Using TensorFlow
16.5.2. Implementation of LSTM Models in Python for Financial Forecasting
16.5.3. Integration of Predictive Models in Financial Planning Tools

16.6. Inventory Automation with Predictive Analytics

16.6.1. Use of Predictive Techniques to Optimize Inventory Management
16.6.2. Apply Predictive Models with Microsoft Azure Machine Learning
16.6.3. Integration of Inventory Management Systems with ERP

16.7. Creation of Automated Financial Reports with Power BI

16.7.1. Automation of Financial Reporting using Power BI
16.7.2. Developing Dynamic Dashboards for Real-Time Financial Analysis
16.7.3. Case Studies of Improvements in Financial Decision Making with Automated Reports 

16.8. Purchasing Optimization with IBM Watson

16.8.1. Predictive Analytics for Purchasing Optimization with IBM Watson
16.8.2. AI Models for Negotiations and Pricing
16.8.3. Integration of AI Recommendations in Purchasing Platforms

16.9. Customer Support with Financial Chatbots and Google DialogFlow

16.9.1. Implementing Financial Chatbots with Google Dialogflow
16.9.2. Integration of Chatbots in CRM Platforms for Financial Support
16.9.3. Continuous Improvement of Chatbots Based on User Feedback

16.10. AI-Assisted Financial Auditing

16.10.1. AI Applications in Internal Audits: Transaction Analysis
16.10.2. Implementation of AI for Compliance Auditing and Discrepancy Detection
16.10.3. Improvement of Audit Efficiency with AI Technologies

Module 17. Strategic Planning and Decision Making with Artificial Intelligence

17.1. Predictive Modeling for Strategic Planning with Scikit-Learn

17.1.1. Building Predictive Models with Python and Scikit-Learn
17.1.2. Application of Regression Analysis in Project Evaluation
17.1.3. Validation of Predictive Models Using Cross-Validation Techniques in Python

17.2. Scenario Analysis with Monte Carlo Simulations

17.2.1. Implementation of Monte Carlo Simulations with Python for Risk Analysis
17.2.2. Use of AI for the Automation and Improvement of Scenario Simulations 
17.2.3. Interpretation and Application of Results for Strategic Decision Making

17.3. Investment Appraisal using IA

17.3.1. AI Techniques for the Valuation of Assets and Companies
17.3.2. Machine Learning Models for Value Estimation with Python
17.3.3. Case Analysis: Use of AI in the Valuation of Technology Startups

17.4. Optimization of Mergers and Acquisitions with Machine Learning and TensorFlow

17.4.1. Predictive Modeling to Evaluate M&A Synergies with TensorFlow
17.4.2. Simulation of Post-M&A Integrations with AI Models
17.4.3. Use of NLP for Automated due Diligence Analysis

17.5. Portfolio Management with Genetic Algorithms

17.5.1. Use of Genetic Algorithms for Portfolio Optimization
17.5.2. Implementation of Selection and Allocation Strategies with Python
17.5.3. Analyzing the Effectiveness of Portfolios Optimized by AI

17.6. Artificial Intelligence for Succession Planning

17.6.1. Use of AI for Talent Identification and Development
17.6.2. Predictive Modeling for Succession Planning using Python
17.6.3. Improvements in Change Management using AI Integration

17.7. Market Strategy Development with AI and TensorFlow

17.7.1. Application of Deep Learning Techniques for Market Analysis
17.7.2. Use of TensorFlow and Keras for Market Trend Modeling
17.7.3. Development of Market Entry Strategies Based on AI Insights

17.8. Competitiveness and Competitive Analysis with AI and IBM Watson

17.8.1. Competitor Monitoring using NLP and Machine Learning
17.8.2. Automated Competitive Analysis with IBM Watson
17.8.3. Implementation of Competitive Strategies Derived from AI Analysis

17.9. AI-Assisted Strategic Negotiations

17.9.1. Application of IA Models in the Preparation of Negotiations
17.9.2. Use of AI-Based Negotiation Simulators for Training Purposes
17.9.3. Evaluation of the Impact of AI on Negotiation Results

17.10. Implementation of AI Projects in Financial Strategy

17.10.1. Planning and Management of AI Projects
17.10.2. Use of Project Management Tools Such as Microsoft Project
17.10.3. Presentation of Case Studies and Analysis of Success and Learning

Module 18. Advanced Financial Optimization Techniques with OR-Tools

18.1. Introduction to Financial Optimization

18.1.1. Basic Optimization Concepts
18.1.2. Optimization Tools and Techniques in Finance
18.1.3. Applications of Optimization in Finance

18.2. Investment Portfolio Optimization

18.2.1. Markowitz Models for Portfolio Optimization
18.2.2. Portfolio Optimization with Constraints
18.2.3. Implementation of Optimization Models with OR-Tools in Python

18.3. Genetic Algorithms in Finance

18.3.1. Introduction to Genetic Algorithms
18.3.2. Application of Genetic Algorithms in Financial Optimization
18.3.3. Practical Examples and Case Studies

18.4. Linear and Nonlinear Programming in Finance

18.4.1. Fundamentals of Linear and Nonlinear Programming
18.4.2. Applications in Portfolio Management and Resource Optimization
18.4.3. Tools for Solving Linear Programming Problems

18.5.  Stochastic Optimization in Finance

18.5.1. Concepts of Stochastic Optimization
18.5.2. Applications in Risk Management and Financial Derivatives
18.5.3. Stochastic Optimization Models and Techniques

18.6. Robust Optimization and its Application in Finance

18.6.1. Fundamentals of Robust Optimization
18.6.2. Applications in Uncertain Financial Environments
18.6.3. Case Studies and Examples of Robust Optimization

18.7. Multi-Objective Optimization in Finance

18.7.1. Introduction to Multiobjective Optimization
18.7.2. Applications in Diversification and Asset Allocation
18.7.3. Techniques and Tools for Multiobjective Optimization

18.8. Machine Learning for Financial Optimization

18.1.1. Application of Machine Learning Techniques in Optimization
18.1.2. Optimization Algorithms Based on Machine Learning
18.1.3. Implementation and Case Studies

18.9. Optimization Tools in Python and OR-Tools

18.9.1. Python Optimization Libraries and Tools (SciPy, OR-Tools).
18.9.2. Practical Implementation of Optimization Problems
18.9.3. Examples of Financial Applications

18.10. Projects and Practical Applications of Financial Optimization

18.10.1. Development of Financial Optimization Projects
18.10.2. Implementation of Optimization Solutions in the Financial Sector
18.10.3. Evaluation and Presentation of Project Results

Module 19. Analysis and Visualization of Financial Data with Plotly and Google Data Studio

19.1. Fundamentals of Financial Data Analysis

19.1.1. Introduction to Data Analysis
19.1.2. Tools and Techniques for Financial Data Analysis
19.1.3. Importance of Data Analysis in Finance

19.2. Techniques for Exploratory Analysis of Financial Data

19.2.1. Descriptive Analysis of Financial Data
19.2.2. Visualization of Financial Data with Python and R
19.2.3. Identifying Patterns and Trends in Financial Data

19.3.  Financial Time Series Analysis

19.3.1. Fundamentals of Time Series
19.3.2. Time Series Models for Financial Data
19.3.3. Time Series Analysis and Forecasting

19.4. Correlation and Causality Analysis in Finance

19.4.1. Correlation Analysis Methods
19.4.2. Techniques for Identifying Causal Relationships
19.4.3. Applications in Financial Analysis

19.5. Advanced Visualization of Financial Data

19.5.1. Advanced Data Visualization Techniques
19.5.2. Tools for Interactive Visualization (Plotly, Dash)
19.5.3. Use Cases and Practical Examples

19.6. Cluster Analysis in Financial Data

19.6.1. Introduction to Cluster Analysis
19.6.2. Applications in Market and Customer Segmentation
19.6.3. Tools and Techniques for Cluster Analysis

19.7. Network and Graph Analysis in Finance

19.7.1. Fundamentals of Network Analysis
19.7.2. Applications of Network Analysis in Finance
19.7.3. Network Analysis Tools (NetworkX, Gephi)

19.8. Text and Sentiment Analysis in Finance

19.8.1. Natural Language Processing (NLP) in Finance
19.8.2. Sentiment Analysis in News and Social Networks
19.8.3. Tools and Techniques for Text Analysis

19.9. Financial Data Analysis and Visualization Tools with AI

19.9.1. Data Analysis Libraries in Python (Pandas, NumPy)
19.9.2. Visualization Tools in R (ggplot2, Shiny)
19.9.3. Practical Implementation of Analysis and Visualization

19.10. Practical Analysis and Visualization Projects and Applications

19.10.1. Development of Financial data Analysis Projects
19.10.2. Implementation of Interactive Visualization Solutions
19.10.3. Evaluation and Presentation of Project Results

Module 20. Artificial Intelligence for Financial Risk Management with TensorFlow and Scikit-Learn

20.1. Fundamentals of Financial Risk Management

20.1.1. Risk Management Basics
20.1.2. Types of Financial Risks
20.1.3. Importance of Risk Management in Finance

20.2. Credit Risk Models with AI

20.2.1. Machine Learning Techniques for Credit Risk Assessment
20.2.2. Credit Scoring Models (Scikit-Learn)
20.2.3. Implementation of Credit Risk Models with Python

20.3. Market Risk Models with AI

20.3.1. Market Risk Analysis and Management
20.3.2. Application of Predictive Market Risk Models
20.3.3. Implementation of Market Risk Models

20.4. Operational Risk and its Management with AI

20.4.1. Concepts and Types of Operational Risk
20.4.2. Application of AI Techniques for Operational Risk Management
20.4.3. Tools and Practical Examples

20.5. Liquidity Risk Models with AI

20.5.1. Fundamentals of Liquidity Risk
20.5.2. Machine Learning Techniques for Liquidity Risk Analysis
20.5.3. Practical Implementation of Liquidity Risk Models

20.6. Systemic Risk Analysis with AI

20.6.1. Systemic Risk Concepts
20.6.2. Applications of AI in the Evaluation of Systemic Risk
20.6.3. Case Studies and Practical Examples

20.7. Portfolio Optimization with Risk Considerations

20.7.1. Portfolio Optimization Techniques
20.7.2. Incorporation of Risk Measures in Optimization
20.7.3. Portfolio Optimization Tools

20.8. Simulation of Financial Risks

20.8.1. Simulation Methods for Risk Management
20.8.2. Application of Monte Carlo Simulations in Finance
20.8.3. Implementation of Simulations with Python

20.9. Continuous Risk Assessment and Monitoring

20.9.1. Continuous Risk Assessment Techniques
20.9.2. Risk Monitoring and Reporting Tools
20.9.3. Implementation of Continuous Monitoring Systems

20.10. Projects and Practical Applications in Risk Management

20.10.1. Development of Financial Risk Management Projects
20.10.2. Implementation of AI Solutions for Risk Management
20.10.3. Evaluation and Presentation of Project Results

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