Master’s Degree in Artificial Intelligence, Model Management and Implementation
Structuralia
Key Information
Campus location
Online
Languages
English
Study format
Distance Learning
Duration
1 year
Pace
Full time, Part time
Tuition fees
EUR 6,490 / per year
Application deadline
Request info
Earliest start date
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Introduction
Why pursue a Master’s Degree in Artificial Intelligence, Model Management, and Implementation?
This Master’s degree has been conceived to respond to the growing need for knowledge and skills in the development of AI models and algorithms demanded by today’s technology-driven market.
As a matter of fact, many specialized staffing companies point to AI as one of the knowledge assets that will be the most demanded in the coming years, considering that the AI sector is expected to have a global business volume of 16 trillion dollars by 2030.
This program has been designed to benefit all professional profiles, with an Introduction to AI basics that does not require extensive prior knowledge of programming and statistics. It has been structured in two large sections: first, a technical section which explores the main Machine Learning and Deep Learning models and algorithms, and the second section addresses its business applications and implications.
Upon completion of the program, the students will have the necessary skills to manage and promote AI projects
Scholarships and Funding
Structuralia 50% Scholarships – Online:
Curriculum
Module I Introduction to Artificial Intelligence (AI)
- Unit 1: Introduction to AI
- Unit 2: Brief History of AI: From Myth to Reality
- Unit 3: Key Concepts, Agents, and Knowledge Representation
- Unit 4: Problem Solving: Automated Reasoning and Search
- Unit 5: Automated Learning: Supervised, Unsupervised, Reinforcement Learning I
- Unit 6: Automated Learning: Supervised, Unsupervised, Reinforcement Learning II
- Unit 7: Big Data: Learning with Million Data
- Unit 8: Human-Machine Interaction: Artificial Vision and Natural Language Processing
- Unit 9: The Future of AI: Ethical Issues and Diversity
Module II Self-service Excel, Talend and Trifacta Data
- Unit 1: Data preparation
- Unit 2: Excel
- Unit 3: Talend Data Preparation
- Unit 4: Trifacta Wrangler
Module III Data Mining, Machine Learning and Deep Learning
- Unit 1: Supervised Learning (I)
- Unit 2: Supervised Learning (II)
- Unit 3: Unsupervised Learning
- Unit 4: Deep Learning
Module IV Advanced Deep Learning
- Unit 1: Supervised Deep Learning (I)
- Unit 2: Supervised Deep Learning (II)
- Unit 3: Unsupervised Deep Learning (I)
- Unit 4: Unsupervised Deep Learning (II)
Module V Data Visualization Tools
- Unit 1: Working with Data in BI Desktop
- Unit 2: DAX in Power BI Desktop
- Unit 3: Advanced Power BI Reporting
- Unit 4: Interactions Microsoft Ecosystem Tools
Module VI Machine Learning, Deep Learning, and Data Science Practical Applications
- Unit 1: Machine Learning
- Unit 2: Deep Learning
- Unit 3: Data Science
- Unit 4: Case study application
Module VII Technology Ecosystems
- Unit 1: Introduction to Technology Ecosystems
- Unit 2: Enabling Technologies I
- Unit 3: Enabling Technologies II
- Unit 4: Enabling Technologies III
Module VIII Ideation Methodologies and Techniques and AI Project Management
- Unit 1: Introduction
- Unit 2: Design Thinking
- Unit 3: Lean Start-up and Scrum
- Unit 4: Application to AI projects
Module IX The Impact of AI on Business
- Unit 1: AI applied to Different Sectors
- Unit 2: AI Applied to Different Business Areas
- Unit 3: AI and Entrepreneurship
- Unit 4: Ethics. Business and Society
Module X Master’s Final Project (MFP)
The program is subject to possible content updates and upgrades,