This 100% online Professional master’s degree will allow you to optimize design and construction processes through tools such as generative modeling, predictive simulation and energy efficiency based on AI”

Artificial Intelligence (AI) is rapidly transforming architecture, offering new tools to design, plan and construct buildings more efficiently and sustainably. The use of AI in architecture has expanded, allowing architects to optimize designs through advanced simulations that take into account variables such as natural light, ventilation and energy consumption.

This is how this Professional master’s degree was created, designed to train architects in the use of advanced technologies to revolutionize the design and construction process. In this sense, it will analyze how Artificial Intelligence can optimize and transform traditional architectural practice. Through the use of tools such as AutoCAD and Fusion 360, as well as an introduction to generative modeling and parametric design, professionals will be able to integrate these innovations into their projects.

Likewise, the use of AI for space optimization and energy efficiency, key elements in contemporary architecture, will be discussed in depth. Using tools such as Autodesk Revit and Google DeepMind, it will be possible to design more sustainable environments through data analysis and advanced energy simulations. This approach will also be complemented by the introduction of smart urban planning, addressing the demands of sustainable design in increasingly complex and urban environments.

Finally, experts will cover cutting-edge technologies such as Grasshopper, MATLAB and laser scanning tools to develop innovative and sustainable projects. In addition, through simulation and predictive modeling, they will be able to anticipate and solve structural and environmental problems before they occur
In this way, TECH has created a detailed, fully online university program, which makes it easy for graduates to access educational materials through any electronic device with an Internet connection. This eliminates the need to travel to a physical location and adapt to a specific schedule. Additionally, it integrates the revolutionary Relearning methodology, which is based on the repetition of essential concepts to improve the understanding of the content.

You will position yourself at the forefront of the industry, leading innovative and sustainable projects that integrate the latest technologies, which will increase your competitiveness and opportunities in the global job market”

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

• Development of practical cases presented by experts in Artificial Intelligence
• The graphic, schematic and practical contents with which it is conceived provide cutting- Therapeutics 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

You will investigate the importance of cultural heritage preservation, using Artificial Intelligence to conserve and revitalize historic structures, thanks to an extensive library of multimedia resources”

The program’s teaching staff includes professionals from the industry who contribute their work experience to this 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 experts in the field of educational coaching with extensive experience.

You will master platforms such as Autodesk Revit, SketchUp and Google DeepMind, developing skills to design more sustainable and efficient environments, hand in hand with the best digital university in the world, according to Forbes"

You'll work with tools like Grasshopper and Autodesk Fusion 360 to create adaptive and sustainable designs, exploring the integration of robotics in construction and customization in digital fabrication"

The program will cover from the basics of Artificial Intelligence and Machine Learning, to advanced techniques of predictive modeling and analysis of massive data applied to architectural design. Therefore, engineers will master simulation and automation tools to optimize construction processes, improving energy efficiency and sustainability. In addition, modules on the use of algorithms for intelligent project management, the creation of virtual environments and the development of adaptive and innovative architectural solutions will be included.

The Professional master’s degree in Artificial Intelligence in Architecture will offer a comprehensive and specialized content, designed for engineers interested in applying cutting-edge technologies in the architectural field”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

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

1.2. Artificial Intelligence in Games

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

1.3. Neural Networks

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

1.4. Genetic Algorithms

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

1.5. Thesauri, Vocabularies, Taxonomies

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

1.6. Semantic Web

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

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

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

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

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

Module 2. Data Types and Data Life Cycle

2.1. Statistics

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

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data
2.2.2. According to Its Shape
2.2.2.1. Numeric
2.2.2.2. Text:
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

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

2.4. Initial Stages of the Cycle

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

2.5. Data Collection

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

2.6. Data Cleaning

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

2.7. Data Analysis, Interpretation and Evaluation of Results

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

2.8. Datawarehouse

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

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

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

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

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

3.3. From Data to Information

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

3.4. Extraction of Information Through Visualization

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

3.5. Data Quality

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

3.6. Dataset

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

3.7. Unbalance

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

3.8. Unsupervised Models

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

3.9. Supervised Models

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

3.10. Tools and Good Practices

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

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

4.1. Statistical Inference

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

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

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

4.4. Missing Values

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

4.5 Noise in the Data

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

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

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

4.8. The Data

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

4.9. Instance Selection

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

4.10. Data Pre-processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

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

5.2. Efficiency and Analysis of Algorithms

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

5.3. Sorting Algorithms

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

5.4. Algorithms with Trees

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

5.5. Algorithms Using Heaps

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

5.6. Graph Algorithms

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

5.7. Greedy Algorithms

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

5.8. Minimal Path Finding

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

5.9. Greedy Algorithms on Graphs

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

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

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

6.2. Agent Architectures

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

6.3. Information and Knowledge

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

6.4. Knowledge Representation

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

6.5. Ontologies

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

6.6. Ontology Languages and Ontology Creation Software

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

6.7. Semantic Web

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

6.8. Other Knowledge Representation Models

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

6.9. Knowledge Representation Assessment and Integration

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

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

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

Module 7. Machine Learning and Data Mining

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

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

7.2. Data Exploration and Pre-processing

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

7.3. Decision Trees

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

7.4. Evaluation of Classifiers

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

7.5. Classification Rules

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

7.6. Neural Networks

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

7.7. Bayesian Methods

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

7.8. Regression and Continuous Response Models

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

7.9. Clustering

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

7.10. Text Mining and Natural Language Processing (NLP)

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

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

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

8.2. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

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

8.4. Union of Layers and Operations

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

8.5. Construction of the First Neural Network

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

8.6. Trainer and Optimizer

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

8.7. Application of the Principles of Neural Networks

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

8.8. From Biological to Artificial Neurons

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

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

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

8.10. Fine Tuning Hyperparameters of Neural Networks

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

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

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

9.2. Reuse of Pre-Trained Layers

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

9.3. Optimizers

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

9.4. Learning Rate Programming

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

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

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

9.7. Transfer Learning

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

9.8. Data Augmentation

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

9.9. Practical Application of Transfer Learning

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

9.10. Regularization

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

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

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

10.2. TensorFlow and NumPy

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

10.3. Model Customization and Training Algorithms

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

10.4. TensorFlow Features and Graphs

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

10.5. 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 training models

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
11.10.1. Edge Detection
11.10.1. Rule-Based Segmentation Methods

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

12.1. Text Generation using RNN

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

12.2. Training Data Set Creation

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

12.3. Classification of Opinions with RNN

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

12.4. Encoder-Decoder Network for Neural Machine Translation

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

12.5. Attention Mechanisms

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

12.6. Transformer Models

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

12.7. Transformers for Vision

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

12.8. Hugging Face’s Transformers Library

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

12.9. Other Transformers Libraries. Comparison

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

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

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

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

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

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

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

13.3. Stacked Automatic Encoders

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

13.4. Convolutional Autoencoders

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

13.5. Noise Suppression of Automatic Encoders

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

13.6. Sparse Automatic Encoders

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

13.7. Variational Automatic Encoders

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

13.8. Generation of Fashion MNIST Images

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

13.9. Generative Adversarial Networks and Diffusion Models

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

13.10. Implementation of the Models

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

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

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

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

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

14.6. Evolutionary Computation Models (II)

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

14.7. Evolutionary Programming Applied to Learning Problems

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

14.8. Multi-Objective Problems

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

14.9. Neural Networks (I)

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

14.10. Neural Networks (II)

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

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

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

15.2. Implications of Artificial Intelligence in Healthcare Service

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

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

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

15.4. Retail

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

15.5. Industry

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

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

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

15.7. Public Administration

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

15.8. Educational

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

15.9. Forestry and Agriculture

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

15.10. Human Resources

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

Module 16. AI-Assisted Design in Architectural Practice

16.1. Advanced AutoCAD Applications with AI

16.1.1. Integration of AutoCAD with AI Tools for Advanced Design
16.1.2. Automation of Repetitive Tasks in Architectural Design with AI
16.1.3. Case Studies Where AI-Assisted AutoCAD Has Optimized Architectural Projects

16.2. Advanced Generative Modeling with Fusion 360

16.2.1. Advanced Generative Modeling Techniques Applied to Complex Projects
16.2.2. Using Fusion 360 to Create Innovative Architectural Designs
16.2.3. Examples of Applying Generative Modeling in Sustainable and Adaptive Architecture

16.3. Optimizing Designs with AI in Optimus

16.3.1. Optimization Strategies for Architectural Design Optimization Using AI Algorithms in Optimus
16.3.2. Sensitivity Analysis and Exploration of Optimal Solutions in Real Projects
16.3.3. Review of Industry Success Stories Using Optimus for AI-Based Optimization

16.4. Parametric Design and Digital Fabrication with Geomagic Wrap

16.4.1. Advances in Parametric Design with AI Integration Using Geomagic Wrap
16.4.2. Practical Applications of Digital Fabrication in Architecture
16.4.3. Outstanding Architectural Projects Using AI-Assisted Parametric Design for Structural Innovations

16.5. AI-Assisted Parametric Design for Structural Innovations

16.5.1. Adaptive and Context Sensitive Design with AI Sensors
16.5.2. Implementing Adaptive Design Using AI and Real-Time Data
16.5.3. Examples of Ephemeral Architecture and Urban Environments Designed with AI

16.6. Analysis of How Adaptive Design Influences the Sustainability and Efficiency of Architectural Projects

16.6.1. Simulation and Predictive Analytics in CATIA for Architects
16.6.2. Advanced Use of CATIA for Architectural Simulation
16.6.3. Implementing Predictive Analytics in Significant Architectural Projects

16.7. Personalization and UX in Design with IBM Watson Studio

16.7.1. IBM Watson Studio's AI Tools for Architectural Personalization
16.7.2. User-Centered Design Using AI Analytics
16.7.3. Case Studies of AI Use Cases for Personalization of Architectural Spaces and Products

16.8. Collaboration and Collective Design Powered by AI

16.8.1. AI-Powered Collaborative Platforms for Design Projects
16.8.2. AI Methodologies that Foster Creativity and Collective Innovation
16.8.3. Success Stories and Challenges in AI-Assisted Collaborative Design

16.9. Ethics and Responsibility in AI-Assisted Design

16.9.1. Ethical Debates in the Use of AI in Architectural Design
16.9.2. Study on Biases and Fairness in AI Algorithms Applied to Design
16.9.3. Current Regulations and Standards for Responsible AI Design

16.10. Challenges and Future of AI-Assisted Design

16.10.1. Emerging Trends and Cutting-Edge Technologies in AI for Architecture
16.10.2. Analysis of the Future Impact of AI on the Architectural Profession
16.10.3. Foresight on Future Innovations and Developments in AI-Assisted Design

Module 17. Space Optimization and Energy Efficiency with AI

17.1. Optimizing Spaces with Autodesk Revit and AI

17.1.1. Using Autodesk Revit and AI for Spatial Optimization and Energy Efficiency
17.1.2. Advanced Techniques for Improving Energy Efficiency in Architectural Designs
17.1.3. Case Studies of Successful Projects Combining Autodesk Revit with AI

17.2. Analysis of Energy Efficiency Metrics and Data with SketchUp and Trimble

17.2.1. Applying SketchUp and Trimble Tools for Detailed Energy Analysis
17.2.2. Developing Energy Efficiency Metrics Using AI
17.2.3. Strategies for Setting Energy Efficiency Goals for Architectural Projects

17.3. Bioclimatic Design and AI-Optimized Solar Orientation

17.3.1. AI-Assisted Bioclimatic Design Strategies for Maximizing Energy Efficiency
17.3.2. Examples of Buildings Using AI-Guided Design to Optimize Thermal Comfort
17.3.3. Practical Applications of AI in Solar Orientation and Passive Design

17.4. AI-Assisted Sustainable Materials and Technologies with Cityzenit

17.4.1. Innovation in Sustainable Materials Supported by AI Analysis
17.4.2. Using AI to Develop and Apply Recycled and Low-Environmental-Impact Materials
17.4.3. Study of Projects Using Renewable Energy Systems Integrated with AI

17.5. Urban Planning and Energy Efficiency with WattPredictor and AI

17.5.1. AI Strategies for Energy Efficiency in Urban Design
17.5.2. Implementing WattPredictor to Optimize Energy Use in Public Spaces
17.5.3. Successful Cases of Cities Using AI to Improve Urban Sustainability

17.6. Intelligent Energy Management with Google DeepMind's Energy

17.6.1. Applications of DeepMind Technologies for Energy Management
17.6.2. Implementing AI for Energy Consumption Optimization
17.6.3. Assessment of Cases Where AI Has Transformed Energy Management in Communities and Buildings

17.7. AI-Assisted Energy Efficiency Certifications and Regulations

17.7.1. Using AI to Ensure Compliance with Energy Efficiency Standards (LEED, BREEAM)
17.7.2. AI Tools for Energy Audit and Certification of Projects
17.7.3. Impact of Regulations on AI-Supported Sustainable Architecture

17.8. Life Cycle Assessment and Environmental Footprint with Enernoc

17.8.1. AI Integration for Life Cycle Analysis of Building Materials
17.8.2. Using Enernoc to Assess Carbon Footprint and Sustainability
17.8.3. Model Projects Using AI for Advanced Environmental Assessments

17.9. Energy Efficiency Education and Awareness with Verdigris

17.9.1. Role of AI in Energy Efficiency Education and Awareness
17.9.2. Using Verdigris to Teach Sustainable Practices to Architects and Designers
17.9.3. Initiatives and Educational Programs Using AI to Promote a Cultural Change Toward Sustainability

17.10. Future of Space Optimization and Energy Efficiency with ENBALA

17.10.1. Exploration of Future Challenges and the Evolution of Energy Efficiency Technologies
17.10.2. Emerging Trends in AI for Spatial and Energy Optimization
17.10.3. Perspectives on How AI Will Continue to Transform Architecture and Urban Design

Module 18. Parametric Design and Digital Fabrication

18.1 Advances in Parametric Design and Digital Fabrication with Grasshopper

18.1.1. Using Grasshopper to Create Complex Parametric Designs
18.1.2. Integrating AI into Grasshopper to Automate and Optimize Design
18.1.3. Flagship Projects Using Parametric Design for Innovative Solutions

18.2. Algorithmic Optimization in Design with Generative Design

18.2.1. Application of Generative Design for Algorithmic Optimization in Architecture
18.2.2. Using AI to Generate Efficient and Novel Design Solutions
18.2.3. Examples of How Generative Design Has Improved the Functionality and Aesthetics of Architectural Projects

18.3. Digital Fabrication and Robotics in Construction with KUKA PRC

18.3.1. Implementing Robotics Technologies such as KUKA PRC in Digital Fabrication
18.3.2. Advantages of Digital Fabrication in Precision, Speed and Cost Reduction
18.3.3. Digital Fabrication Case Studies Highlighting Successful Integration of Robotics in Architecture

18.4. Adaptive Design and Manufacturing with Autodesk Fusion 360

18.4.1. Using Fusion 360 to Design Adaptive Architectural Systems
18.4.2. Implementing AI in Fusion 360 for Mass Customization
18.4.3. Innovative Projects Demonstrating the Potential for Adaptability and Customization

18.5. Sustainability in Parametric Design with Topology Optimization

18.5.1. Applying Topology Optimization Techniques to Improve Sustainability
18.5.2. Integrating AI to Optimize Material Usage and Energy Efficiency
18.5.3. Examples of How Topological Optimization Has Improved the Sustainability of Architectural Projects

18.6. Interactivity and Spatial Adaptability with Autodesk Fusion 360

18.6.1. Integrating Real-Time Data and Sensors to Create Interactive Architectural Environments
18.6.2. Using Autodesk Fusion 360 in Adapting Design in Response to Environmental or Usage Changes
18.6.3. Examples of Architectural Projects Using Spatial Interactivity to Improve User Experience

18.7. Efficiency in Parametric Design

18.7.1. Applying Parametric Design to Optimize Sustainability and Energy Efficiency of Buildings
18.7.2. Using Simulations and Life Cycle Analysis Integrated with AI to Improve Green Decision-Making
18.7.3. Cases of Sustainable Projects Where Parametric Design Has Been Crucial

18.8. Mass Customization and Digital Fabrication with Magic (Materialise)

18.8.1. Exploring the Potential of Mass Customization through Parametric Design and Digital Fabrication
18.8.2. Applying Tools such as Magic to Customize Architectural and Interior Design
18.8.3. Outstanding Projects Showcasing Digital Fabrication in the Customization of Spaces and Furniture

18.9. Collaboration and Collective Design Using Ansys Granta

18.9.1. Using Ansys Granta to Facilitate Collaboration and Decision Making in Distributed Design
18.9.2. Methodologies to Improve Innovation and Efficiency in Collaborative Design Projects
18.9.3. Examples of How AI-Enhanced Collaboration Can Lead to Innovative and Sustainable Results

18.10. Challenges and the Future of Digital Fabrication and Parametric Design

18.10.1. Identifying Emerging Challenges in Parametric Design and Digital Manufacturing
18.10.2. Future Trends and the Role of AI in the Evolution of These Technologies
18.10.3. Discussion of How Continuous Innovation Will Affect Architectural Practice and Design in the Future

Module 19. Simulation and Predictive Modeling with AI

19.1. Advanced Simulation Techniques with MATLAB in Architecture

19.1.1. Using MATLAB for Advanced Architectural Simulations
19.1.2. Integrating Predictive Modeling and Big Data Analytics
19.1.3. Case Studies Where MATLAB Has Been Fundamental in Architectural Simulation

19.2. Advanced Structural Analysis with ANSYS

19.2.1. Implementing ANSYS for Advanced Structural Simulations in Architectural Projects
19.2.2. Integrating Predictive Models to Evaluate Structural Safety and Durability
19.2.3. Projects Highlighting the Use of Structural Simulations in High Performance Architecture

19.3. Modeling Space Use and Human Dynamics with AnyLogic

19.3.1. Using AnyLogic to Model the Dynamics of Space Use and Human Mobility
19.3.2. Applying AI to Predict and Improve the Efficiency of Space Use in Urban and Architectural Environments
19.3.3. Case Studies Showing How Simulation Influences Urban and Architectural Planning

19.4. Predictive Modeling with TensorFlow in Urban Planning

19.4.1. Implementing TensorFlow for Modeling Urban Dynamics and Structural Behavior
19.4.2. Using AI to Predict Future Outcomes in City Design
19.4.3. Examples of How Predictive Modeling Influences Urban Planning and Design

19.5. Predictive Modeling and Generative Design with GenerativeComponents

19.5.1. Using GenerativeComponents to Merge Predictive Modeling and Generative Design
19.5.2. Applying Machine Learning Algorithms to Create Innovative and Efficient Designs
19.5.3. Examples of Architectural Projects that Have Optimized Their Design Using These Advanced Technologies

19.6. Simulation of Environmental Impact and Sustainability with COMSOL

19.6.1. Applying COMSOL for Environmental Simulations in Large-Scale Projects
19.6.2. Using AI to Analyze and Improve the Environmental Impact of Buildings
19.6.3. Projects that Show How Simulation Contributes to Sustainability

19.7. Simulation of Environmental Performance with COMSOL

19.7.1. Applying COMSOL Multiphysics for Environmental and Thermal Performance Simulations
19.7.2. Using AI to Optimize Design Based on Daylighting and Acoustics Simulations
19.7.3. Examples of Successful Implementations That Have Improved Sustainability and Comfort

19.8. Innovation in Simulation and Predictive Modeling

19.8.1. Exploration of Emerging Technologies and Their Impact on Simulation and Modeling
19.8.2. Discussion of How AI Is Changing Simulation Capabilities in Architecture
19.8.3. Evaluation of Future Tools and Their Potential Applications in Architectural Design

19.9. Simulation of Construction Processes with CityEngine

19.9.1. Applying CityEngine to Simulate Construction Sequences and Optimize On-Site Workflows
19.9.2. AI Integration for Modeling Construction Logistics and Coordinating Activities in Real-Time
19.9.3. Case Studies Showing Improved Construction Efficiency and Safety through Advanced Simulations

19.10. Challenges and Future of Simulation and Predictive Modeling

19.10.1. Assessment of Current Challenges in Simulation and Predictive Modeling in Architecture
19.10.2. Emerging Trends and the Future of These Technologies in Architectural Practice
19.10.3. Discussion on the Impact of Continued Innovation in Simulation and Predictive Modeling in Architecture and Construction

Module 20. Heritage Preservation and Restoration with AI

20.1. AI Technologies in Heritage Restoration with Photogrammetry

20.1.1. Using Photogrammetry and AI for Accurate Heritage Documentation and Restoration
20.1.2. Practical Applications in the Restoration of Historic Buildings
20.1.3. Outstanding Projects Combining Advanced Techniques and Respect for Authenticity

20.2. Predictive Analysis for Conservation with Laser Scanning

20.2.1. Implementing Laser Scanning and Predictive Analytics in Heritage Conservation
20.2.2. Using AI to Detect and Prevent Deterioration in Historic Structures
20.2.3. Examples of How These Technologies Have Improved Accuracy and Efficiency in Conservation

20.3. Cultural Heritage Management with Virtual Reconstruction

20.3.1. Applying AI-Assisted Virtual Reconstruction Techniques
20.3.2. Strategies for Digital Heritage Management and Preservation
20.3.3. Success Stories in the Use of Virtual Re-Enactment for Education and Preservation

20.4. Preventive Conservation and AI-Assisted Maintenance

20.4.1. Using AI Technologies to Develop Strategies for Preventive Conservation and Maintenance of Historic Buildings
20.4.2. Implementing AI-Based Monitoring Systems for Early Detection of Structural Problems
20.4.3. Examples of How AI Contributes to the Long-Term Conservation of Cultural Heritage

20.5. Digital Documentation and BIM in Heritage Preservation

20.5.1. Applying Advanced Digital Documentation Techniques, including BIM and Augmented Reality, Assisted by AI
20.5.2. Using BIM Models for Efficient Heritage Management and Restoration
20.5.3. Case Studies on the Integration of Digital Documentation in Restoration Projects

20.6. AI-Assisted Preservation Policies and Management

20.6.1. Using AI-Based Tools for Management and Policy Making in Heritage Preservation
20.6.2. Strategies for Integrating AI into Conservation-Related Decision-Making
20.6.3. Discussion of How AI Can Improve Collaboration Among Institutions for Heritage Preservation

20.7. Ethics and Responsibility in AI Restoration and Preservation

20.7.1. Ethical Considerations in the Application of AI in Heritage Restoration
20.7.2. Debate on the Balance between Technological Innovation and Respect for Historical Authenticity
20.7.3. Examples of How AI Can Be Used Responsibly in Heritage Restoration

20.8. Innovation and the Future of Heritage Preservation with AI

20.8.1. Perspectives on Emerging AI Technologies and Their Application in Heritage Preservation
20.8.2. Assessing the Potential of AI to Transform Restoration and Conservation
20.8.3. Discussion on the Future of Heritage Preservation in an Era of Rapid Technological Innovation

20.9. Cultural Heritage Education and Awareness with GIS

20.9.1. Importance of Public Education and Awareness in Cultural Heritage Preservation with GIS
20.9.2. Using Geographical Information Systems (GIS) to Promote the Valuation and Knowledge of Cultural Heritage
20.9.3. Successful Education and Outreach Initiatives Using Technology to Teach about Cultural Heritage

20.10. Challenges and the Future of Heritage Preservation and Restoration

20.10.1. Identification of Current Challenges in Cultural Heritage Preservation
20.10.2. Role of Technological Innovation and AI in Future Conservation and Restoration Practices
20.10.3. Perspectives on How Technology Will Transform Heritage Preservation in the Coming Decades

This multidisciplinary approach will enable you to develop technical and strategic skills that will transform your ability to tackle complex challenges in construction and design”