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About this course…
Machine Learning in Practice is a short course that focuses on applications of machine learning in a variety of fields. Machine learning has flipped the business world upside down with its superior ability and applicability to various areas. After course completion, individuals should expect to:
- Understand the different types of machine learning and some popular methods in each.
- Be able to use machine learning methods in real-world applications.
- Be able to determine applicable machine learning techniques for different datasets.
- Understand how to use machine learning to learn features from high dimensional or complex data.
- Understand the ethical implications of machine learning models.
Topics that will be covered in the course include:
- Machine learning definitions and differences from Artificial Intelligence (AI)
- Supervised learning methods
- Unsupervised learning methods
- Feature learning techniques
- Deep learning (FFNN, CNN, RNN, GANs)
- Applications in Computer Vision
- Applications in Natural Language Processing
- Reinforcement learning methods
More information about the course content is available. Learn more…
Who should attend?
This short course gives individuals hand-on experience with machine learning that they can apply to their area of expertise. This course is intended for all individuals interested in learning more about machine learning, how they can apply it to a variety of application areas and how it can apply to their field. The following are examples of individuals who would gain the most from this course:
- Scientists with some introductory programming experience interested in learning about the applications of machine learning in their field.
- Researchers with some introductory programming experience interested in using machine learning for publications.
- Software engineers with extensive technical experience, looking to learn about machine learning, available software libraries, and how to incorporate machine learning in general purpose products.
Regardless of area of expertise, all individuals should have some introductory programming experience, ideally with the Python programming language.
The course program highlights topics that will be covered in the course. Learn more…
Continuing education credits
Colorado School of Mines will award 2.5 Continuing Education Credits (CEUs) to participants who complete this course.
Registration for this course is open now. Enrollment is limited; therefore, applications will be accepted in the order received. Full information about fees, options, and payment methods is available. Learn more…
Travel and accommodations
Registrants are responsible for their own travel arrangements, transportation, lodging, and meals. Additional information is available through the links below.
Dr. Hua Wang is an expert in machine learning research and is an Associate Professor in the Department of Computer Science at Colorado School of Mines. Learn more…
Saad Elbeleidy is currently a PhD candidate working with Dr. Hua Wang in the MInDS@Mines team. Learn more…
Location and parking
This course will be taught at Catalyst HTI, located in the RiNo neighborhood of Denver, Colorado. Learn more…
Further technical information
For more information about the course content, please contact:
Dr. Hua Wang
Department of Computer Science
Colorado School of Mines
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