PRNN_2024

Course Feedback Form - https://forms.gle/EHVd94owuwwZZkKt9

Team's Channel - PRNN_2024, Code: dgmf3u4

Broad course contents: 

1. Supervised Learning, Empirical Risk Minimization

2. Bayesian Decision Theory and Optimality

3. Density Estimation: Parametric and Non-Parametric Techniques

4. Nearest Neighbor classifiers

5. Linear Models, Gradient-based optimization

6. Risk Decomposition, Bias Variance tradeoff

7. Regularization and Model Evaluation 

8. Kernel Machiens and SVM 

9. Perceptron, Neural Networks, Back Propagation

10. Decision Trees

11. Ensemble Methods: Bagging and Boosting 

12.  Unsupervised Learning: Feature Selection - Principal Component Analysis

13. Unsupervised Learning: Clustering - Mixture models and K-Means  

Pre-requisites:

1.     Mandatory: A course on probability theory  (e.g., STOMA, Probability and Random Processes

2.    Mandatory: Moderate programming skills in Python

3.   Optional: A course on optimization theory

Reference Materials:

1. Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David, Cambridge University Press

2. Hart, Peter E., David G. Stork, and Richard O. Duda. Pattern classification. Hoboken: Wiley, 2000.

3.       Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006.