Master's Degree in Artificial Intelligence: Model Management and Implementation
English
Credits: 60 ECTS
PRESENTATION
Objectives
The overall objective is to equip students with the necessary knowledge and tools to understand, manage, and lead AI initiatives and projects within an organization.
The overall objective can be achieved through the following specific objectives:
1. Understand the basic concepts of AI, its limitations, and its possibilities.
2. Learn the programming languages, tools, and platforms used in AI projects.
3. Become familiar with the most commonly used machine learning and deep learning algorithms.
4. Analyze and learn about other technologies to develop innovative and distinctive business models.
5. Learn the main AI project management methodologies.
6. Understand the implications and applications of AI in the various functional areas of a company.
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world cases, digital tools, and support from a team of specialized instructors.
Program
The State of the Art in Artificial Intelligence
Philosophy of Artificial Intelligence
The Future of Artificial Intelligence
Project Development Process Using Artificial Intelligence
Data: Your Greatest Asset
Machine learning
Deep learning
Transformers
Generation of synthetic data
Hyperparameters in Artificial Intelligence Models
Linear Regression
Nonlinear regression and support vector machines (SVM)
Decision trees, random forests
Fuse logic and gradient down
Recommendation systems
Setting Up the Development Environment: Anaconda, Visual Studio Code, and Python
Input dataset and data preprocessing
TensorHub, TensorFlow, and Keras
Image Processing
Generation of Artificial Intelligence Models
Introduction
Self-Service Solutions
Data Processing Techniques
Data Quality Management
Types of Data Problems
Data Cleaning with Excel
DATASET
Functions. Part I
Functions. Part II
Functions. Part III
Instructions for Installing Talend Data Preparation Free Desktop
Data Cleansing with Talend Data Preparation
Basic cleansing functions
Data Normalization
Data Enrichment
Registration Instructions
Data Cleansing with Trifacta
Basic cleansing functions
Data Normalization
Data Enrichment
Introduction
Simple, Multiple, and Logistic Linear Regression (I)
Simple, Multiple, and Logistic Linear Regression (II)
Support vector machines (SVM)
Decision Trees
KNN (k-nearest neighbors)
Naive Bayes
Evaluation of Supervised Models
Sample Exercise
Proposed exercise
Introduction to Clustering: PurConsider and Metrics
K-means clustering
Hierarchical clustering, other techniques, and examples
Principal Component Analysis (PCA)
PCA Example Exercise
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Sample Exercise
Proposed exercise
Introduction
Review: Artificial Neural Network (ANN)
Review: ANN Exercises
Convolutional Neural Networks (CNN)
CNN Exercises
Natural Language Processing (I)
Recurrent Neural Networks (RNN) (I)
Recurrent Neural Networks (RNN) (II)
Natural Language Processing (II)
RNN Exercise
Boltzmann Machines (BM)
Restricted Boltzmann Machines (RBM)
Recommendation systems
Recommendation systems: metrics
RBM exercise
Self-organizing maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Proposed exercise
Introduction to Power BI
Different Types of Power BI
First Simple Report
Power Query: Data Sources
Data Transformation
Data Modeling
Starting with DAX (I)
Starting with DAX (II)
Becoming Proficient in DAX (I)
Becoming Proficient in DAX (II)
Table and Matrix
Trends
How to Properly Filter Your Data
Bookmarks
Drill Through
An In-Depth Look at the Power BI Service
Sharing Content in Power BI Service
Comparing Power BI Service and Power BI Report Server
Integrating Python and R in Power BI Desktop
Introducing Bravo for Power BI Desktop
Linear Regression
Logistic regression
Basic Neural Network
Clustering
Principal Component Analysis (PCA)
Deep learning
Optimization
Convolutional Neural Network
Recurrent Neural Network
Natural Language Processing (NLP)
Creating Tables and Reports
Data transformation and filtering
Data Visualization
Relationship between data tables
Dashboard
Object Detection in Images
Object Classification in Images
Facial recognition
Word detection
Business Intelligence application
The Fourth Industrial Revolution
Digital Transformation in Companies
Fundamentals and Key Points
Benefits
Enabling technologies
Big data
Cloud computing
Blockchain
Artificial Intelligence
Augmented and Virtual Reality
BIM
Collaborative robots
Additive Manufacturing
Hyperconnectivity
IoT
Manufacturing Execution System (MES)
Process Integration and Efficiency
Use Cases
New methodologies: agile, lean startup, or design thinking
Business Change Management
Introduction
Key Elements in AI Project Management
AI Project Characteristics
Introduction to the Main Agile and Ideation Methodologies
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows you to track progress through dashboards and reports.
It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
It is based on best practices in security, access control, learning traceability, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
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