May 17,2024

005 - Training an AI Model: From Theory to Application

 

Training an AI Model: From Theory to Application

 

Course Number: 005

 

Fee: Free

 

Length: 4 Hours

 

Credits: This course qualifies for 4 PDH (unstructured) and 4 CEU (structured) technical learning credits issued by most US State Engineering Regulatory Organizations and all Canadian Provincial and Territorial Engineering Regulatory Organizations.

 

Online, Self-Directed Format: Primarily Text with Illustrations and Video

 

Course Description (Syllabus):

 

Welcome to the course "Training an Engineering AI Model: From Theory to Application"

 

An increasing number of engineers in every field of engineering are incorporating the benefits of artificial intelligence (AI) into their design or management workflow. However, every AI model, whether an “off-the-shelf” model or building an AI model from scratch, needs to be trained to perform the specific tasks required by the engineer.

 

Therefore, if you are an engineer contemplating incorporating AI into your professional practise, it is essential that you understand the fundamentals of AI model training.

 

This comprehensive course is designed to provide you, the engineer, with a solid understanding of AI as it is applied in engineering and AI model training, taking you from theories and concepts to practical applications in various engineering industries.

 

The course begins with an introduction to AI model training, where we will discuss the definition and importance of AI model training. We will then delve into the theoretical background of AI and machine learning, providing you with a strong foundation for understanding the principles and techniques used in AI model training. This will include a discussion on the different stages involved in AI model training and their significance in building accurate and efficient models.

 

As we move forward, the course will cover various theories and techniques behind AI model training. We will explain key concepts and explore the different approaches to machine learning, including supervised, unsupervised, and reinforcement learning. Furthermore, we will delve into the mathematical foundations of AI, covering topics such as linear algebra, probability and statistics, calculus, algorithms, and models. We will also discuss various neural networks, decision trees, support vector machines, and k-nearest neighbors, providing you with a comprehensive understanding of the different models used in AI model training.

 

The course will then move on to the practical application of AI model training, where we will cover topics such as data sets applicable to different fields of engineering, preprocessing data, data cleaning, data transformation, feature selection, and extraction. We will also discuss training the model, choosing the right machine learning algorithm, and evaluating and tuning the model. Additionally, we will cover important concepts such as overfitting and underfitting, hyperparameter tuning, and performance metrics, providing you with the skills needed to build and optimize AI models.

 

Finally, the course will provide an up-to-date list of tools to assist in the training of an AI model and explore future trends and challenges in AI model training, including advancements in the field, ethical considerations, and career prospects.

 

Understanding AI Model training is essential for any engineer considering incorporating Artificial Intelligence into their professional practise or an engineer using AI who wishes to improve the scope, accuracy and efficiency of their AI Model.

 

Learning Outcomes: By completing this course you will learn:

 

1. Overview of AI and AI Model Training

 

* Definition and Applications

* Types and Sources of Engineering Software Platforms with Pretrained AI Models

 

2. AI Theories and Concepts

 

* Machine, Deep, Supervised, Unsupervised and Reinforced Learning

* Mathematical Concepts

* Understanding Algorithms

* Understanding Neural Networks, Decision Trees, Vector Machines and K- Nearest Neighbours

 

3. Model Training

 

* Feature Selection and Extraction

* Choosing the Right Architecture and Algorithms

* Testing, Validation, Evaluation and Performance Metrics

* Overfitting, Underfitting, Hyperparameter Tuning

* Importance of Data in AI Model Training

* Collecting and Processing Data

* List of Data Collection and Processing Software Tools

* List of AI Model Software Training Tools

 

3. Data

 

* How to Use Datasets in AI Model Training

* Specialized Datasets, types and Source List for Different Fields of Engineering: Civil/Structural, Mechanical, Electrical, Chemical, Geotechnical, Biomedical and Environmental Engineering

 

5. Conclusion

 

* Advancements and the Future of AI Model Training

* Ethical Considerations

* Continued Learning

 

Who Should Take This Course:

 

Engineering students and every engineer who intends to incorporate AI into their professional practise or wishes to improve the scope, accuracy and efficiency of their existing AI Model.

 

Course Instructions:

 

Enrollment: You must be registered and logged into the Engineer eLearning Centre website to access this online, self-directed course.

 

Once you are logged into your Engineer eLearning account click “Browse Courses” to find this course. Click Enroll and confirm you wish to enroll.

 

Once you have confirmed your enrollment you will be returned to your account “My Courses”. Click Enter Course in course profile box. You can enter the course at any time and as many times as you wish. There is no time limit as to when you must complete this course. To access the course learning content click the module headings in the side menu.

 

Credit Options

 

After completing this course you have the option of receiving eMETA PDH or CEU credit(s) or both. eMETA credits qualify for Unstructured and Structured technical learning credits required by most US State and all Canadian Provincial and Territorial Engineering Regulatory Organizations.

 

 To receive your eMETA CEU credit you will be required to take an automated quiz.

 

Instructions on how to receive your credit(s) and download your PDF Certificate(s) are provided in the side menu of this course. Your credits will also be registered in your Engineer eLearning Centre account. You can download a Transcript of all credits earned in the Engineer eLearning Centre by clicking the Transcript tab in your account.

 

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