With this 100% online Professional master’s degree, you will obtain the tools and knowledge to implement AI solutions that optimize financial processes, such as accounting automation and risk management”

Why Study at TECH?

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

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

At TECH Global University

Innovation

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

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

The Highest Standards

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

95% of TECH students successfully complete their studies.

Networking

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

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

Empowerment

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

500+ collaborative agreements with leading companies.

Talent

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

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

Multicultural Context

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

TECH students represent more than 200 different nationalities.  

Learn with the best

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

Teachers representing 20 different nationalities. 

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

Analysis

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

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

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

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

The program will include in-depth specialization in financial process automation, which enables entrepreneurs to optimize the management of repetitive tasks and improve operational efficiency. It will also cover predictive modeling and advanced data analytics techniques to support strategic decision making and financial optimization strategies with sophisticated tools. In addition, entrepreneurs will be able to implement AI solutions for financial risk management and to use data visualization platforms to interpret financial information effectively. 

The content of the Professional master’s degree has been carefully designed to address the specific needs of entrepreneurs seeking to transform their financial operations through technology” 

Syllabus

The syllabus of this Professional master’s degree has been designed to provide comprehensive specialization in the latest technologies and methodologies that are revolutionizing the financial sector. In a first block, the automation of financial processes through advanced AI techniques will be addressed. This will include learning about tools and systems that optimize the management of repetitive tasks, such as invoice processing and bank reconciliation, enabling professionals to improve accuracy and efficiency in financial administration. 

In addition, the focus will be on strategic planning and decision making, layered with the latest technology and tools to improve the efficiency and accuracy of financial management. They will also be able to apply analysis and simulation techniques to formulate decisions based on accurate data, which is crucial to adapt to a dynamic and competitive economic environment. They will also develop a more robust strategic vision based on quantitative information. 

Finally, advanced financial optimization and data analysis techniques will be analyzed, becoming familiar with tools such as OR-Tools for portfolio optimization, as well as advanced techniques for financial data visualization and analysis, with Plotly and Google Data Studio. In turn, advanced methods for financial risk management will be addressed through AI models developed with TensorFlow and Scikit-learn, ensuring that experts are prepared to face modern financial challenges with innovative and data-driven solutions. 

In this way, TECH has developed a complete university program in a fully online mode, allowing graduates to access the teaching materials from any device with an Internet connection. This eliminates the need to travel to a physical center and to adapt to fixed schedules. Additionally, it incorporates the innovative Relearning methodology, which is based on the repetition of key concepts to ensure optimal understanding of the content.

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

Module 1. Fundamentals of Artificial Intelligence
Module 2. Data Types and Data Life Cycle
Module 3. Data in Artificial Intelligence
Module 4. Data Mining: Selection, Pre-Processing and Transformation
Module 5. Algorithm and Complexity in Artificial Intelligence
Module 6. Intelligent Systems 
Module 7. Machine Learning and Data Mining
Module 8. Neural Networks, the Basis of Deep Learning
Module 9. Deep Neural Networks Training
Module 10. Model Customization and training with TensorFlow
Module 11. Deep Computer Vision with Convolutional Neural Networks
Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention
Module 13. Autoencoders, GANs and Diffusion Models
Module 14. Bio-Inspired Computing
Module 15. Artificial Intelligence: Strategies and Applications
Module 16. Automation of Financial Department Processes with Artificial Intelligence
Module 17. Strategic Planning and Decision Making with Artificial Intelligence
Module 18. Advanced Financial Optimization Techniques with OR-Tools
Module 19. Analysis and Visualization of Financial Data with Plotly and Google Data Studio
Module 20. Artificial Intelligence for Financial Risk Management with TensorFlow and Scikit-Learn

Where, When and How is it Taught?

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

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

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

1.2. Artificial Intelligence in Games

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

1.3. Neural Networks

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

1.4. Genetic Algorithms

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

1.5. Thesauri, Vocabularies, Taxonomies

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

1.6. Semantic Web

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

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.1. 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 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
2.3.2. 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 Result Evaluation

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. 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. Adam and RMSprop Optimizers 
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 API tfdata 

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. Loading TFRecord Files with TensorFlow 
10.7.3. Using TFRecord files for training models 

10.8. Keras Preprocessing Layers 

10.8.1. Using the Keras Preprocessing API 
10.8.2. Construction of preprocessing pipelined 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. Training a model 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 Cortex Visual 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. Classification and Localization in Deep Computer Vision 

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. Segmentation methods based on rules 

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 Transformer 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 Computing 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 the 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. Implementation of 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. IA Applications in Internal Audits: Transaction Analysis
16.10.2. Implementation of IA for Compliance Auditing and Discrepancy Detection
16.10.3. Improvement of Audit Efficiency with IA 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. IA 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 IA-Based Negotiation Simulators for Training Purposes
17.9.3. Evaluation of the Impact of IA on Negotiation Results

17.10. Implementation of IA Projects in Financial Strategy

17.10.1. Planning and Management of IA 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
18.2.3. Portfolio Optimization with Constraints
18.2.4. 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

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences"