Course Description

Machine learning is the art and science of designing algorithms and developing programs that can accomplish specific goals and objectives without explicit instructions. This course is a broad overview of machine learning and will encompass supervised and unsupervised learning including topics from regression, ensemble learning, kNN, kMeans, support vector machines and principal component analysis.

Course Outline

  • The modules are designed to be taken in the following order Module 1: Machine Learning fundamentals & essentials part I, Machine Learning fundamentals & essentials part II, Supervised Learning, Unsupervised Learning
  • Delivery: Asynchronous, self-paced, 10-12 hours a week.
  • Sequence for each module: Instructional videos, suggested readings, video embedded quizzes, short quiz after each module milestone, a final quiz for each module.
  • Students who are confident about the material can test out of the module and advance to the next one by completing the final quiz for that module.
  • Failing the final quiz three times for a module will result in recycling the entire module.

Learner Outcomes

Students successfully completing this course will be able to:
  1. Discuss fundamentals and essentials of Machine Learning (ML) and ML-related topics, including statistical modeling, Artificial Intelligence (AI), etc.
  2. Differentiate the types of ML, including supervised learning, unsupervised learning, reinforcement learning, etc.
  3. Compare and contrast ML algorithms developed for typical learning tasks, including regression, classification, clustering, and dimensional reduction, etc.
  4. Examine similarity metrics, distance metrics, and performance evaluation metrics appropriate for each learning task.
  5. Outline a typical ML pipeline of each learning task in terms of input, process, output, and performance evaluation.
  6. Recognize software tools, libraries, and platforms developed for specific ML tasks.
  7. Apply appropriate ML algorithms to real-life problems.
  8. Assess the performance of ML algorithms with appropriate evaluation metrics.
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