Course prepared for Sofia University: Palo Alto facility, October-December 2023.
- (2023-12-03) Lecture10 has been added
- (2023-11-15) Lecture07 has been added
- (2023-11-04) Lecture06 has been added
- (2023-10-29) Lecture05 has been added
- (2023-10-22) Lecture04 has been added
- (2023-10-15) Lecture03 has been added
- (2023-10-08) Lecture02 has been added
- (2023-09-25) Lecture01 has been added
- (2023-09-25) Repo has been created
| N | Lecture | Desctription |
|---|---|---|
| 01 | Introduction | Introduction. Course logistics and syllabus. Historical reference. ML Paradigms |
| 02 | Supervised Learning | Supervised Learning. Features. Loss Functions. Cross-validation |
| 03 | ML Model Fitting Problems | Empirical and Structural Risk. Error Decomposition. Model Selection. Underfitting and overfitting |
| 04 | k-NN for Classification | Non-parametric Classification: k-NN Method and its variants. Common Metrics. Classification Mean Error |
| 05 | k-NN for Regression | Non-parametric Regression: k-NN Method and its variants. Bias-Variance trade-off for k-NN Regression. Mean (Absolute) Test Error |
| 06 | Linear Regression | Linear Regression and its variants. ML and MAP principles. Regression Quality Metrics |
| 07 | Classification Metrics | Classification Metrics. Binary and Multi-Class cases |
| 10 | AI/ML/DL Buzzwords | AI/ML/DL "Buzzwords": Broad Concepts, Directions, and State-of-the-Art |