list_of_exercises.md

List of Exercises

1. Linear and Logistic Regression with Error Estimation
   1. Linear Regression
      1. Implementation of Linear Regression(from scratch and using scikit learn)
   2. Logistic Regression
      1. Preprocessing and implementing Logistic regression on titanic dataset using scikit learn
      2. Logistic regression for diabetes prediction(From scratch vs using Scikit learn)
2. Implementation of Univariate and Multivariate Gaussian Densities
   1. Generating and visualizing univariate and multivariate gaussian distributions
3. Dimensionality Reduction using Principal Component Analysis (PCA)
   1. Implementation of PCA in Iris Dataset(From Scratch and using Scikit Learn)
4. Clustering Algorithms
   1. k-Means
   2. Implementing K-means Clustering from scratch and using scikit learn
   3. Gaussian mixture modeling (GMM)
   4. Implenenting GMM from scratch using expectation and maximization algorithm.
5. Classification Algorithms
   1. Back Propagation Neural Network (BPNN)
   2. Implementing BPNN from scratch
   3. Support Vector (SVM)
   4. Implementing SVM from scratch using CVXOPT
6. Construction of Decision Tree and Random Forest
   1. Implementing Decision Tree and Random Forest classifier in Kyphosis dataset using Scikit Learn.
7. Implementation of Convolution Neural Network (CNN)
8. Sequence Prediction using Recurrent Neural Network (RNN)
9. Isolated-Word Speech Recognition
10. Face Detection and Tracking
11. Object Recognition