Thanks to the highest quality online training, you will be able to transform the way in which the challenges of the pharmaceutical sector are addressed. You will improve patient outcomes and optimize resource management!”

The importance of Artificial Intelligence in Pharmacy lies in its ability to optimize processes, improve accuracy in decision-making and offer a more personalized approach to healthcare, which has a direct impact on patients' quality of life. In this sense, it has contributed to the analysis of large volumes of data, the automation of routine or administrative tasks and advances in pharmaceutical research.

This Professional master’s degree in Artificial Intelligence in Pharmacy from TECH will offer a unique opportunity to address the most salient aspects of this constantly evolving sector. Through a comprehensive approach, professionals will be prepared to lead the implementation of innovative technologies in the pharmaceutical sector. They will gain an in-depth understanding of how AI is transforming everything from personalizing treatments to optimizing processes and improving patient safety. They will also know how to use advanced AI tools to manage large volumes of data, detect drug-drug interactions, design drugs and automate administrative processes, enabling them to provide a more efficient and accurate service to patients. 

From this, specialists will not only have acquired a set of highly demanded skills, but will also have taken a decisive step towards a promising future in the pharmaceutical field. In turn, they will be prepared to be agents of change in the integration of Artificial Intelligence in Pharmacy, improving health outcomes and the patient experience through the use of of technology.

TECH has also thought about the excellence and flexibility of the students, therefore, offers this postgraduate course in 100% online mode, providing the convenience to be trained at the time and place that best fits their daily obligations. Additionally, this is complemented with the Relearning methodology, created to internalize the concepts in a more agile and productive way, without having to invest many hours of study.

Thanks to the Relearning system, you will be able to master the academic contents in a more natural and progressive way, preparing you efficiently to be part of the technological revolution in healthcare”

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

• The development of practical cases presented by experts with a deep mastery of Artificial Intelligence in Pharmacy
• 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

This is the time to take the next step and secure a prominent position in one of the most innovative and promising areas of healthcare. Enroll now and learn at your own pace thanks to the online modality!”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this specializing 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.

By learning about AI in Pharmacy, you will be able to personalize treatments, optimize processes and improve pharmaceutical care. You will boost your future with the best academic tools provided by TECH"

In this Professional master’s degree, you will receive training from renowned experts and access up-to-date content on the use of AI in medication management, drug design and more"

This academic pathway will provide advanced training for professionals seeking to be at the forefront of technological innovation. Throughout the program, they will discover how AI tools are transforming Pharmacy, from the personalization of treatments to the optimization of processes in pharmaceutical management. In addition, the agenda will address key areas such as the use of AI in drug design, automation of administrative processes and improving the safety of drug dispensing. In this way, you will become familiar with the latest technologies used in research and development in the industry.

You will take the next step in your professional career, acquiring the necessary skills to become a reference in the Pharmacy of the future. Get ready to transform the pharmaceutical sector with AI!”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

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

1.2. Artificial Intelligence in Games

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

1.3. Neural Networks

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

1.4. Genetic Algorithms

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

1.5. Thesauri, Vocabularies, Taxonomies

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

1.6. Semantic Web

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

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

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

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

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

Module 2. Data Types and Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences

2.1.2.1 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. Sorting by Merge (Merge_Sort)
5.3.6. Sorting Quickly (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 Matrices
7.4.2. Numerical Evaluation Matrices
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. Operations

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. Graph Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Pre-Processing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction with Keras
10.8.3. Using the Keras Pre-Processing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Data Pre-Processing 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. Using 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 Pre-Processing
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. 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 Studies
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 Artificial Intelligence in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Studies

15.3. Risks Related to the Use of Artificial Intelligence in Health Services

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

15.4. Retail

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

15.5. Industry

15.5.1. Implications of Artificial Intelligence in Industry. Opportunities and Challenges
15.5.2. Case Studies

15.6. Potential Risks Related to the Use of Artificial Intelligence in the Industry

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

15.7. Public Administration

15.7.1. Implications of Artificial Intelligence in Public Administration. Opportunities and Challenges
15.7.2. Case Studies
15.7.3. Potential Risks Related to the Use of Artificial Intelligence
15.7.4. Potential Future Developments / Uses of Artificial Intelligence

15.8. Educational

15.8.1. Implications of Artificial Intelligence in Education. Opportunities and Challenges
15.8.2. Case Studies
15.8.3. Potential Risks Related to the Use of Artificial Intelligence
15.8.4. Potential Future Developments / Uses of Artificial Intelligence

15.9. Forestry and Agriculture

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

15.10. Human Resources

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

Module 16. Management and Analysis of Biomedical Information and Scientific Literature with Artificial Intelligence

16.1. Introduction to the Use of AI for Biomedical Information

16.1.1. Importance of Biomedical Information in Pharmacy
16.1.2. Challenges in the Management and Analysis of the Scientific Literature
16.1.3. Role of AI in the Management of Large Volumes of Scientific Data
16.1.4. Examples of AI Tools such as Semantic Scholar in Biomedical Research

16.2. Biomedical Information Retrieval with AI

16.2.1. Advanced Searching Techniques in Scientific Databases
16.2.2. AI Algorithms to Improve Search Accuracy and Relevance
16.2.3. Personalization of Results through Machine Learning
16.2.4. Applications such as PubMed AI for Efficient Information Retrieval

16.3. Natural Language Processing (NLP) in Scientific Texts

16.3.1. NLP Applications in the Analysis of Biomedical Literature
16.3.2. Automatic Extraction of Key Information from Scientific Articles
16.3.3. Automatic Summarization and Generation of Structured Abstracts
16.3.4. Tools such as SciBERT for Scientific Text Processing

16.4. Biomedical Text Mining

16.4.1. Basic Concepts and Techniques in Text Mining
16.4.2. Identification of Trends and Patterns in Scientific Publications
16.4.3. Extraction of Relationships between Biomedical Entities
16.4.4. Examples such as MEDLINE and Text Mining Library for Text Mining

16.5. Ontologies and Semantic Annotations in Biomedicine

16.5.1. The Use and Creation of Ontologies in the Health Sciences
16.5.2. Semantic Annotation of Scientific Documents
16.5.3. AI for Semantic Enrichment and Contextual Searching
16.5.4. Tools such as BioPortal and UMLS for Ontological Management

16.6. Scientific Literature Recommender Systems

16.6.1. Recommendation Algorithms in Scientific Platforms
16.6.2. Personalization of Content for Researchers and Practitioners
16.6.3. AI in Predicting Future Relevance and Citations
16.6.4. Applications such as Mendeley Suggest and ResearchGate

16.7. Visualization of Biomedical Data and Knowledge

16.7.1. Visualization Techniques for Complex Biomedical Data
16.7.2. Knowledge Maps and Research Networks
16.7.3. AI Tools to Visualize Relationships and Trends
16.7.4. Examples such as VOSviewer and Cytoscape in Scientific Visualization

16.8. AI-Assisted Knowledge Discovery

16.8.1. Identification of New Hypotheses from Existing Data
16.8.2. Integration of Multidisciplinary Data with AI
16.8.3. Prediction of Unknown Drug Interactions and Pharmacological Effects
16.8.4. Cases such as IBM Watson Discovery and Elsevier's Entellect

16.9. Big Data Management in Biomedical Research

16.9.1. Challenges of Big Data in Biomedical Research
16.9.2. Efficient Storage and Processing of Massive Data
16.9.3. AI for Genomic and Proteomic Data Analysis
16.9.4. Tools such as Apache Hadoop and Spark in Biomedicine

16.10. Challenges and Future Perspectives in NLP for Scientific Literature

16.10.1. Specific NLP Challenges in Scientific and Biomedical Data
16.10.2. Limitations in Search and Analysis Automation
16.10.3. Recent Advances in NLP for Biomedical Sciences (BioGPT, BioBERT)
16.10.4. Future Applications of AI in Scientific Research and Publication

Module 17. Development of New Drugs with Artificial Intelligence

17.1. Identification of Therapeutic Targets with AI

17.1.1. Concept of Therapeutic Targets and Their Importance in Pharmacology
17.1.2. AI Algorithms for the Identification of Potential Targets
17.1.3. Neural Network Models in Therapeutic Target Prediction
17.1.4. Examples such as Insilico Medicine for Target Discovery

17.2. AI-Assisted Drug Design

17.2.1. AI-Assisted Molecular Design Techniques
17.2.2. Computational Modeling in Drug Design
17.2.3. Molecule Generation with Deep Learning
17.2.4. Applications such as Atomwise in Drug Discovery

17.3. Pharmaceutical Compound Optimization

17.3.1. Optimization Processes in Drug Development
17.3.2. AI Techniques for Improving Composite Properties
17.3.3. Molecular Simulation Tools in Drug Optimization
17.3.4. Examples of Platforms such as Schrodinger for Optimization

17.4. Simulation of Drug-Receptor Interactions

17.4.1. Importance of Drug-Receptor Interactions
17.4.2. Molecular Simulation Techniques in Pharmacology
17.4.3. AI Algorithms for Predicting Molecular Interactions
17.4.4. Tools such as Cresset for Interaction Simulation

17.5. Generation of Bioactive Compound Libraries

17.5.1. Creation of Compound Libraries in Drug Development
17.5.2. AI in the Generation and Classification of Compounds
17.5.3. Virtual Screening of Bioactive Compounds
17.5.4. Example of Tools such as Chemoinformatics from ChemAxon

17.6. Preclinical Hypothesis Validation with AI

17.6.1. Preclinical Stage Hypothesis Validation
17.6.2. AI Models for Testing in Preclinical Experimentation
17.6.3. Predictive Analytical Tools for Preclinical Analysis
17.6.4. Case of BenevolentAI in Preclinical Research

17.7. Prediction of Side Effects and Toxicity

17.7.1. Assessment of Side Effects by AI
17.7.2. Toxicity Models in Early Stages of Development
17.7.3. AI for Drug Safety and Toxicity Analysis
17.7.4. DeepChem Applications for Composite Toxicity

17.8. Dose and Formulation Optimization

17.8.1. Principles of Formulation and Dose Optimization
17.8.2. AI in the Determination of Effective and Safe Dose
17.8.3. Predictive Models for Formulation Optimization
17.8.4. Genentech Example for Dose and Formulation Studies

17.9. In Silico Tests in Early Development Phases

17.9.1. Concept of in Silico Testing in Pharmaceutical Development
17.9.2. Algorithms for Simulation and Virtual Testing
17.9.3. AI in In Vitro and In Vivo Test Reduction
17.9.4. Example of Simulations Plus in In Silico Prediction

17.10. AI-Assisted Clinical Studies

17.10.1. AI-Assisted Clinical Study Design
17.10.2. Optimization of the Recruitment Phase in Clinical Trials
17.10.3. Response Modeling and Follow-Up in Clinical Trials
17.10.4. Cases such as Medidata Solutions in Clinical Trial Optimization

Module 18. Artificial Intelligence in Diagnostics and Personalized Therapies

18.1. Early Diagnosis of Diseases

18.1.1. Importance of Early Diagnosis in the Treatment of Diseases
18.1.2. AI Algorithms for Early Detection of Pathology
18.1.3. AI for Predictive Analysis of Risk Factors
18.1.4. Examples such as PathAI for Automated Diagnosis

18.2. AI-Based Personalized Therapies

18.2.1. Introduction to Personalized Medicine and Its Relevance
18.2.2. AI for Personalization of Treatments according to Patient Profile
18.2.3. Predictive Models for Personalized Dose Adjustment
18.2.4. Applications such as Tempus in Personalized Oncology

18.3. Biomarker Detection Using AI

18.3.1. Concept and Types of Biomarkers in Medicine
18.3.2. AI Algorithms for the Identification of Key Biomarkers
18.3.3. Importance of Biomarkers in Diagnosis and Treatment
18.3.4. Tools such as Freenome for Biomarker Detection

18.4. Genomic Medicine and Pharmacogenomics

18.4.1. Genomics and Pharmacogenomics for Personalization of Therapies
18.4.2. AI Applications in the Analysis of Genetic Profiling
18.4.3. AI in the Study of Genetic Variations for Personalized Medicine
18.4.4. Cases such as 23andMe in Personalized Genetic Analysis

18.5. AI in Immunotherapy and Oncology

18.5.1. Introduction to Immunotherapy and Its Impact on Cancer Treatment
18.5.2. Application of AI to Personalize Immune Therapies
18.5.3. AI Models for Optimizing Efficacy of Immunotherapies
18.5.4. Examples such as GNS Healthcare for Immunotherapy in Oncology

18.6. Personalized Pharmacological Counseling

18.6.1. Importance of Personalized Pharmacological Counseling
18.6.2. AI for Treatment Recommendations according to Specific Conditions
18.6.3. AI Models to Optimize Drug Selection
18.6.4. Example of IBM Watson for Oncology in Treatment Recommendations

18.7. Treatment Response Prediction

18.7.1. AI Techniques for Predicting Responses to Different Treatments
18.7.2. Predictive Models of Efficacy and Safety of Treatments
18.7.3. AI Algorithms for Treatment Personalization
18.7.4. Tools such as Foundation Medicine for Analysis of Treatment Response 

18.8. Development of Algorithms for Specific Therapies

18.8.1. Principles of Algorithm Development for Targeted Therapies
18.8.2. AI for Identifying and Developing Targeted Therapies
18.8.3. Algorithms Personalized according to Disease Type
18.8.4. Applications such as Owkin in Federated Learning for Oncology

18.9. Remote Patient Monitoring

18.9.1. Importance of Remote Monitoring in Chronic Patients
18.9.2. AI for Monitoring Parameters and Vital Signs Remotely
18.9.3. Predictive Models to Anticipate Patient Complications
18.9.4. Tools such as Biofourmis for Remote Monitoring

18.10. AI in Portable Diagnostic Devices

18.10.1. Impact of Portable Devices on Health Diagnosis
18.10.2. AI Algorithms in Portable Devices Data Analysis
18.10.3. AI for Real-Time Detection of Health Conditions
18.10.4. Examples such as Butterfly iQ, AI-Assisted Portable Ultrasound

Module 19. Artificial Intelligence in Pharmaceutical Production and Distribution

19.1. Optimization of Manufacturing Processes with AI

19.1.1. Introduction to Pharmaceutical Manufacturing and Current Challenges
19.1.2. AI Algorithms to Improve Production Efficiency
19.1.3. Predictive Models to Reduce Manufacturing Times
19.1.4. Siemens Pharma Example for Process Automation

19.2. Quality Control in Drug Manufacturing

19.2.1. Importance of Quality Control in the Pharmaceutical Industry
19.2.2. AI Algorithms for Inspection and Defect Detection
19.2.3. AI to Ensure Consistency in Product Quality
19.2.4. Applications such as Aizon for Quality Analysis in Production

19.3. AI for Inventory and Distribution Management

19.3.1. Introduction to Inventory Management in Pharmaceuticals
19.3.2. AI Models for Inventory and Demand Optimization
19.3.3. Demand Forecasting Using Data Analytics
19.3.4. Tools such as SAP Integrated Business Planning

19.4. Predictive Maintenance in Production Plants

19.4.1. Concept of Predictive Maintenance and Its Benefits
19.4.2. AI Algorithms to Anticipate Machinery Failures
19.4.3. AI to Optimize Maintenance Cycles
19.4.4. Examples of Digital GE in Predictive Maintenance

19.5. Drug Counterfeit Detection

19.5.1. Impact of Drug Counterfeiting on Public Health
19.5.2. AI for Authentication of Pharmaceutical Products
19.5.3. Computer Vision Algorithms for Counterfeit Detection
19.5.4. Tools such as TruTag for Authenticity Verification

19.6. Automation in Packaging and Labeling

19.6.1. Packaging Processes in the Pharmaceutical Industry
19.6.2. AI for Optimization of Automated Labeling and Packaging
19.6.3. Computer Vision Techniques in Label Control
19.6.4. Rockwell Automation Applications in Packaging

19.7. Logistics Optimization and Safe Distribution of Pharmaceuticals

19.7.1. Drug Logistics and Its Impact on Availability
19.7.2. AI Algorithms for Optimization of Distribution Routes
19.7.3. AI for Tracking Deliveries and Transport Conditions
19.7.4. Examples such as UPS Healthcare for Secure Distribution

19.8. AI for Cold Chain Improvement in Distribution

19.8.1. Importance of the Cold Chain for Sensitive Medicines
19.8.2. Predictive Models for Maintaining Optimal Temperatures
19.8.3. Real-Time Monitoring Algorithms
19.8.4. Tools such as Carrier Sensitech for Cold Chain Control

19.9. Automation of Stock Management in Pharmacies

19.9.1. Introduction to Stock Management in Pharmacies
19.9.2. AI Algorithms for Optimizing Product Replenishment
19.9.3. AI Systems for Demand and Consumption Forecasting
19.9.4. Applications such as Omnicell for Automated Inventory Management 

19.10. Delivery Route Optimization with AI

19.10.1. Delivery Challenges in the Pharmaceutical Industry
19.10.2. Route Optimization Algorithms for Efficient Delivery
19.10.3. AI for Real-Time Dynamic Route Planning
19.10.4. Example of DHL SmartSensor for Drug Logistics

Module 20. Regulation, Safety and Ethics of Artificial Intelligence in Pharmaceuticals

20.1. AI Regulations for Pharmaceutical Products

20.1.1. Introduction to Regulatory Standards in AI Applied to Health Care
20.1.2. Main Regulatory Agencies (FDA, EMA) and Their Role in AI
20.1.3. Standards for the Approval of AI Technologies in Pharmaceuticals
20.1.4. Examples of AI Software Certification for Healthcare Products 

20.2. Healthcare AI Regulatory Compliance

20.2.1. Key Concepts in AI Regulatory Compliance
20.2.2. Legal Requirements for the Development of AI in Pharmacy
20.2.3. AI Audits to Ensure Regulatory Compliance
20.2.4. Examples of AI Compliance under the European MDR

20.3. Data Security in AI Applications

20.3.1. Introduction to Data Security in the Healthcare Environment
20.3.2. Security Protocols for the Storage of Medical Data
20.3.3. AI for Threat Detection and Data Protection
20.3.4. Microsoft Azure Tools for Secure Data Management

20.4. Privacy and Ethics in AI Applications

20.4.1. Ethical Concepts in Patient Data Management
20.4.2. Responsible AI and Privacy Principles in Pharmacy
20.4.3. Tools for Anonymization of Sensitive Data
20.4.4. Examples of Privacy in Google Health

20.5. Transparency of Algorithms in AI for Health

20.5.1. Importance of Transparency in AI Applied to Health
20.5.2. Explainability of Algorithms and Their Interpretation in Healthcare
20.5.3. Methods to Ensure Transparency in AI Models
20.5.4. Application of IBM Explainable AI for Healthcare

20.6. Avoiding Biases in AI Systems

20.6.1. Identification of Biases in Medical and Pharmaceutical Data 
20.6.2. Techniques for Minimizing Bias in AI Algorithms
20.6.3. Examples of Common Biases in AI for Pharmaceuticals
20.6.4. Use of Google's Fairness Toolkit to Reduce Biases

20.7. Auditing AI Systems in Pharmacy

20.7.1. Concept and Objectives of AI Auditing in Health Care
20.7.2. Audit Methods to Validate AI Systems
20.7.3. Audit Criteria to Ensure Quality and Ethics
20.7.4. Example of an AI Audit with TÜV SÜD

20.8. Informed Consent in AI Health Data

20.8.1. Importance of Consent in the Use of Personal Data
20.8.2. AI Tools for Informed Consent Management
20.8.3. AI in Obtaining and Secure Storage of Consents
20.8.4. Example of Consent Management in Epic Systems

20.9. AI for Pharmacy Fraud Detection

20.9.1. Impact of Fraud in the Pharmaceutical Industry
20.9.2. AI Algorithms for Identification of Fraudulent Activities
20.9.3. AI in the Prevention of Counterfeiting and Illegal Sale of Pharmaceuticals
20.9.4. Example of SAS Fraud Framework for Healthcare

20.10. Responsibility and Accountability in AI

20.10.1. Concept of Accountability in AI Applications
20.10.2. Definition of Roles and Responsibilities in AI for Health Care 
20.10.3. AI for Tracking Decisions and Actions in Healthcare Processes
20.10.4. Initiatives such as Partnership on AI for Accountability Guidelines

With this program you will not only boost your theoretical knowledge, but also equip yourself with the practical tools to innovate, lead and transform the future of Pharmacy”