Pattern Recognition and Machine Learning (PRML) Lab

This repository contains the implementation of some basic concepts of Machine Learning as part of PRML lab work. The list of topics are as follows:

1. Lab_01: Probability Theory
2. Lab_02: Linear Algebra
3. Lab_03: Convex Optimization
4. Lab_04: Clustering Part 1 (Kmeans, GMM)
5. Lab_05: Clustering Part 2 (DBSCAN, Heirarchical)
6. Lab_06: Regression Part 1 (Linear (Univariate and Multivariate))
7. Lab_07: Regression Part 2 (Polynomial, Logistic)
8. Lab_08: Classification (KNN, SVM)
9. Lab_09: Dimesionality Reduction (PCA, LDA)
10. Lab_10: Naive Bayes Classifier
11. Lab_11: Hidden Markov Model (HMM)

The Folder contains the following files:

• Skeleton notebook file (*.ipynb)
• PDF version of skeleton notebook file (*.pdf)
• Additional datasets if used in notebook
• Solution notebook file (*_Solution.ipynb)