MAchine Learning Projects

Contents of each project

CA1

• Bayes Theorem
• Naiive Bayes
• Optimal Bayes
• Maximum Likelihood Estimator
• MAP Estimator

CA2

• MSE
• Logistic Regression
• L1/L2 Regularization
• Overfitting/Underfitting

CA3

• Parzen Window
• Distance Metrics
  • Eulcidean
• K-Nearest-Neighbour
• Bias-Variance Tradeoff
• Multi-Layer Perceptron
• Decision Tree

CA4

• SVM
  • Soft-Margin
  • Hard-Margin
• Ensemble Learning

CA5

• PCA
• LDA
• KMeans
• Gaussian Mixture Model
• VC Dimensions

Final Project

• Voice Classification
• Voice Clustering

Supplementary Project

• Electroencephalography Analysis