Advice: Please, LOVE and UNDERSTAND probability, statistics, and linear algebra before you register for this course.
- Course syllabus.
- Video Lectures.
- Link to book webpage. (You can download it in pdf format!). Other two great references (not mandatory for the course) are Duda, Hart, and Stork; and Bishop.
- Lecture Notes
- Code and Programs.
- List of useful texts.
- UCI Machine Learning Repository has many real-life data sets.
- DataCamp assignments
| 01. | Introduction |
| 02. | Introduction to Statistical Decision Theory I (Regression) |
| 03. | Introduction to Statistical Decision Theory II (Classification) |
| 04. | Introduction to Statistical Decision Theory III (Classification) |
| 05. | Getting to "Learning" I (Regression) |
| 06. | Getting to "Learning" II (Classification) |
| 07. | Linear Models for Regression: Least Mean Square |
| 08. | Linear Models for Regression: Centered Model |
| 09. | Linear Models for Regression: Performance |
| 10. | Linear Models for Regression: Data Preprocessing and Transformation |
| 11. | Bias-Variance Decomposition |
| A1. | fast revision on basics of Probability |
| 12. | fast revision on basics of Statistics |
- Ch2, Duda, Hart, and Stork: solve 2, 6, 7, 8, 9, 27, 33; and computer exercises 2, 3.
- LLR simulation
- Linear models
- Linear models (cont.)
- Linear models (cont.), and this one
- Linear models for classification
- Linear models for classification (2)
**Problems on Appendix (revision):**
Please, download guidelines, and suggested projects.
Please, find here samples for exams.