Software Engineering for Machine Learning

I am developing a new course  Software Engineering for Machine Learning (SER 594) to be offered in Spring 2022.

This course: (a) presents frameworks and tools for developing and incorporating machine-learning components into software systems; and (b) examines the application, adaptation, and extension of software engineering practices to develop and adopt machine-learning-enabled robust, secure, and scalable systems.

Syllabus

Arizona State University.
School of Computing and Augmented Intelligence.
version Spring 2022

Lectures

This course will include 26 lectures:

  1. Applied machine learning QuickStart
  2. Data, data processing, data cleaning, and sampling
  3. Fundamentals on supervised learning
  4. Fundamentals on unsupervised learning
  5. Understanding classification,
  6. Understanding regression
  7. Understanding clustering
  8. Neural networks
  9. Building ML applications
  10. Software architecture for ML applications
  11. ML Patterns
  12. Midterm Review
  13. Public available Datasets and Repositories
  14. Machine Learning Libraries
  15. Comparing Libraries
  16. Working with Weka,
  17. Working with DeepLearning4J,
  18. Working with mallet,
  19. Working with Encog
  20. Working with Apache Products
  21. Assembling applications for:
  22. Text Mining
  23. Recommendation Engines
  24. Pattern (Image) recognition
  25. Anomaly (outliers) detection
  26. Final Review