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Course Description

The Artificial Intelligence & Machine Learning Certificate Program at the University of Miami offers 3 individual Certificate of Completion in progressive format: Fundamentals, Elite and Grand Master, and it is intended for individuals with an active interest in seeking jobs in the high-demand Artificial Intelligence-related fields. During each level of the program, students will explore topics, technology, and skills required in the successful application of AI/ML techniques to support key industry needs and demands.

Our Artificial Intelligence (AI) and Machine Learning (ML) Fundamentals level has no prerequisites and aims to provide learners with a comprehensive coverage of core concepts and technologies for AI/ML while steering perspective talents to potential technique areas. In this introductory level, we use an application-driven organization that covers a much broader range of technologies in key application areas. We also cover mathematical skills and programming tools when needed. This approach allows us to cover exciting technologies earlier in the course while providing rigorous foundations and skills needed for innovative research and development.

Course Outline

  • Concepts
  • Tabular Data Analytics and decision support
  • Computer Vision
  • Understanding Natural Language

Learner Outcomes

Upon successful completion of the AI/ML Fundamentals level, learners should be able to demonstrate proficiency in:

  • Representing and visualizing data in an effective manner.
  • Using features to represent data.
  • Preprocessing data before it can be used in AI related tasks. 
  • Computing the performance metrics of classifiers and comparing them.
  • Computing the histogram of an image.
  • Transforming an image by converting it to gray scale, reassigning colors, matching a histogram, changing contrast, filtering by different methods, etc.
  • Understanding how language is represented and processed in the realm of AI.
  • Loading, editing, and saving an image programmatically.
  • Understanding the basic principles of artificial neural networks (architecture, training).
  • Applying the most common techniques to extract features from a dataset or image. 
  • Understanding what classification in AI means and how it is achieved.
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