Syllabus

Note: The detailed course material can be modified.

For the Cont. Case, we will use the slides from the previous lecture.

Lecture - 1. Tuesday: Introduction, 

Lecture -1. Thursday:  Talk. Considerations for a Successful Vision Model Service: A Case Study of Face Recognition

Lecture - 2. Tuesday: ML and Data Systems Fundamentals

Lecture - 2. Thursday: ML and Data Systems Fundamentals

Lecture - 3. Tuesday:  Holiday

Lecture - 3. Thursday: Training Data

Lecture - 4. Tuesday: Apologies for Class Cancleation

Lecture - 4. Thursday: Feature engineering

Lecture - 5. Tuesday: Model selection, development, and training

Lecture - 5. Thursday: Model selection, development, and training

Lecture - 6. Tuesday: Evaluation

Lecture - 6. Thursday: Evaluation

Lecture - 7. Tuesday: Deployment

Lecture-7. Thursday: Deployment

Lecture-8. Tuesday: Test week

Lecture-8. Thursday: Midterm

Lecture - 9. Tuesday: Model Training

Lecture - 9. Thursday: Model Training

Lecture - 10. Tuesday: Continual Learning

Lecture - 10 Thursday: Continual Learning

Lecture - 11. Tuesday: Continual Learning

Lecture - 11. Thursday: Continual Learning

Lecture - 12. Tuesday: Active Learning

Lecture - 12. Thursday: Active Learning

Lecture - 13. Tuesday: Active Learning

Lecture - 13. Thursday: Active Learning

Lecture - 14. Tuesday: ML Infrastructure and Platform

Lecture - 14. Thursday: ML Infrastructure and Platform

Lecture - 15. Tuesday: ML Infrastructure and Platform

Lecture - 15. Thursday: ML Infrastructure and Platform

Lecture-16. Tuesday: Test week

Lecture-16. Thursday: Final Exam