This repository contains files related to Pattern Recognition and Machine Learning Lab (Autumn 2022).
Lab 1 - Probability Theory
- Sampling from Uniform Distribution
- Sampling from Gaussian Distribution
- Categorical Sampling
- Central Limit Theorem
- Computing π using Sampling
- Monty Hall Problem
Lab 2 - Linear Algebra
- System of Equations: Full Rank, Square Matrix
- System of Equations: Full Rank, Non-Square Matrix
- System of Equations: Non-Full Rank Matrix
Lab 3 - Convex Optimization
- Minimizing
$f(x) = x^{2} + x + 2$ - Minimizing
$f(x) = xsinx$ - Minimizing
$f(x, y) = x^{2} + y^{2} + 2x + 2y$ - Minimizing
$f(x, y) = xsinx + ysiny$
Lab 4 - Clustering - I
- Partition Based Clustering (K-Means)
- Model Based Clustering (GMM)
- Applications of Clustering: Iris Flower Dataset
Lab 5 - Regression - I
- Fitting of a Line
- Fitting of a Plane
- Fitting of an M-Dimensional Hyperplane
- Applications of Regression: Salary Prediction
Lab 6 - Regression - II
- Polynomial Regression
- The Shortcomings of Linear Regression
- Logistic Regression
- Classification of Circular-Separated data using LogReg
- MultiClass Logistic Regression
Lab 7 - Clustering - II
- Density Based Clustering (DBSCAN)
- Partition Based Clustering (Fuzzy C-Means)
- Hierarchial Clustering (Agglomerative Approach)
- Applications of Clustering: MNIST Digit Dataset
Lab 8 - Classification
- Support Vector Machines
- K-Nearest Neighbours
- Applications of Classification: MNIST Digit Dataset
Lab 9 - Naïve Bayes
- Binary Classification
- Sentiment Analysis
Lab 10 - Hidden Markov Model
- Evaluation Problem (Forward, Backward Algorithms)
- Learning Problem (Baum Welch Algorithm)
- Decoding Problem (Viterbi Algorithm)
- Using decoder from hmmlearn package
Lab 11 - Dimensionality Reduction
- PCA (Principal Component Analysis)
- LDA (Linear Discriminant Analysis)
Let your plans be dark and impenetrable as night, and when you move, fall like a thunderbolt. — Sun Tzu, The Art of War
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