COURS.

APERÇU

After participating in this webinar, you will be able to:

• Identify and clearly define a problem for an AI application, understanding how to select the most appropriate AI approach based on specific needs and objectives.
• Understand the importance of data preparation, including collection techniques, cleaning, and the significance of data quality for the success of an AI project.
• Master the steps of developing and deploying an AI model, from building and training the model to its evaluation, deployment, and maintenance.

Description
This webinar aims to provide a practical understanding of the essential steps to effectively apply AI technologies. Participants will learn how to define a problem and select the appropriate AI approach, a fundamental aspect for the success of any AI project. Next, we will address data preparation, a crucial step often underestimated, highlighting the importance of data quality. Finally, the webinar will cover the development and deployment of an AI model, offering advice on building, training, evaluating, optimizing, and maintaining models.

This session is designed to offer a comprehensive and accessible overview of implementing AI projects.

Who Should Attend
Anyone interested in learning about the key processes involved in applying AI technologies, including:

• Chemical Engineering
• Civil Engineering
• Environmental Engineering
• General Engineering
• Industrial Engineering

Horaire : 12:00 PM - 1:30 PM EDT

Exigences techniques

Pour les utilisateurs de PC
OS: Windows 7, 8, 10 ou plus récent

Navigateur :
IE 11 ou plus récent, Edge 12 ou plus récent, Firefox 27 ou plus récent, Chrome 30 ou plus récent

Pour les utilisateurs de Macintosh
OS: MacOS 10.7 ou plus récent

Navigateur :
Safari 7+, Firefox 27+, Chrome 30+

iOS
OS: iOS 8 ou plus récent

Android
OS: Android 4.0 ou supérieur

voir le programme complet

PROGRAMME

• Step 1: Problem Definition
  - Identification of needs and objectives
  - Selection of the appropriate AI approach
• Step 2: Data Preparation
  - Data collection and cleaning
  - Importance of data quality
• Step 3: Model Development and Deployment
  - Model building and training
  - Evaluation and optimization
  - Deployment and maintenance

FORMATEUR

Hossein Bonakdari, Ph.D., P.Eng.

Hossein Bonakdari, Department of Civil Engineering Engineering, Ottawa University.

Hossein Bonakdari has worked for several organizations, most recently as a faculty member of the Department of Civil Engineering at the University of Ottawa, Ontario. He has supervised several Ph.D. and MSc students with teaching experience of more than 16 years in the field of Artificial Intelligence application in Civil and Environmental Engineering.

His fields of specialization and interest include the practical application of soft computing techniques in engineering problems. Results obtained from his research have been published in more than 300 papers in international journals (h-index=53). He has also had more than 150 presentations at national and international conferences. He published three books. Dr. Bonakdari's exceptional research achievements have garnered global recognition, consistently placing him in the top 2% of the world's top scientists across various fields for four consecutive years (2019-2023).

Dr. Bonakdari is currently leading several research projects in collaboration with industrial partners.