Module I: Artificial Intelligence and Machine Learning
- Define the characteristics of AI applications.
- Understanding the concepts and benefits of AI.
- Explain the use cases and applications of AI.
- Artificial Intelligence as a lever for innovation: research or technology transfer?
- Introduction to Machine Learning.
- The role of machine learning within Artificial Intelligence.
- Create a simple machine learning model.
- Optimize and adjust the model.
- Distinguish between various AI solutions across a variety of industries and use cases.
- Identify a suitable machine learning solution to solve a given business problem.
Module II: Introduction to Strategic Intelligence and Analysis Methodologies
- Fundamentals of Strategic Intelligence.
- Intelligence Cycle.
- Data Analysis Methodologies.
- Information Sources.
- Collection Techniques.
- Scenario Analysis.
- Integration of Intelligence into Organizational Strategy.
- Tools and Support Technologies.
- Evaluation and Continuous Improvement.
Module III: How to create value with AI in industry
- What technologies to evaluate and how to manage the implementation process, for example, with the aim of optimizing production, improving quality, or reducing costs.
- What applying AI to business processes and systems can mean: industrial applications of AI, for example, automated quality control, predictive machine maintenance, or optimized supply chain management.
- Evaluate the suitability of an enterprise application for natural language processing.
- The importance of data; The Artificial Intelligence pipeline.
- The current challenges of Artificial Intelligence.
- Applications and practical cases in predictive maintenance.
Module IV: Industrial Robotics
- Introduction to industrial robotics: applications and market for robots.
- Introduction to mobile robotics: from robot vacuum cleaner to autonomous car.
- Introduction to computer vision and its possible applications in industry: from quality control of processes and products to robot guidance.
Module V: Industry 4.0 Observatory
- Industry 4.0: vision and key enabling technologies.
- Presentation and analysis of case studies of manufacturing companies that have adopted AI in production.
- Maintenance approaches: to failure, preventive, condition-based, predictive.
- Data-driven techniques for predictive maintenance, with application to industrial use cases.
- Develop a roadmap for an organization to gain strategic advantages through the use of artificial intelligence.
- Presentation and analysis of case studies of manufacturing companies that have adopted AI in production.
Module VI: Artificial intelligence in business and society
- Industry 4.0: vision and key enabling technologies.
- Maintenance approaches: to failure, preventive, condition-based, predictive.
- Data-driven techniques for predictive maintenance, with application to industrial use cases.
- AI for active preservation and access to sound documents.
- Automatic defect analysis based on computer vision techniques and neural networks.
- Automatic manipulation recognition based on clustering and classification.
Module VII: Business Transformation
- Stages of the technological and digital transformation process.
- Alignment with business strategies.
- Requirements identification.
- Process analysis to improve efficiency.
- Criteria for selecting solutions and suppliers.
- Project cost estimation and benefits analysis).
- Project management.
- Change management.
- Risk analysis and management.
- Communication and collaboration through digital channels.
- Leadership.
- Team management.
Module VIII: Practical Cases from the World of Manufacturing
Real-world examples of manufacturing companies that have successfully implemented AI-based solutions: results achieved, challenges faced, and strategies adopted to overcome them.
Practical examples of the use of sustainability data in companies: the intelligent use of data can help improve the sustainability of companies by reducing waste, optimizing the use of resources and reducing environmental impact.
Module IX: Deep Learning
- Deep learning: introduction and general concepts.
– Deep learning for image analysis.
– Applicability of deep learning and datasets.
– Deep learning vs traditional view.
Module X: Idea generation workshop to define a possible AI solution
- Put your acquired knowledge into practice by working together to devise and develop AI-based solutions for specific business challenges with guidance from industry experts and through hands-on exercises and brainstorming sessions.
- Let's create a model together: practical training on a no-code platform to put your knowledge into practice by using the construction of artificial intelligence models.
- Presentation of the proposed solution: This will include a presentation of the solutions developed during the co-design workshop and a discussion of their potential applications in a business environment. Practical advice and suggestions will be provided for successfully implementing the presented strategies and technologies.
*One class hour is equivalent to 45 minutes.
The University of Piura reserves the right to postpone, reschedule, or cancel the program if the minimum number of participants is not met. Special sessions and exams may be scheduled outside of regular class hours, after prior notification to the students.