HW1: Exploring probabilistic methods for parameter estimation. Variance/Covariance and Expectations
HW2: Implementing Ridge Regression (Linear Regression with L2 regularization)
HW3: Implementing Logistic Regression for Classification by using Stochaistic Gradient Descent (SGD) for optimization.
HW4: Implementing SVM with Quadratic Programming. Implementation of Hard Negative Mining to improve training accuracy.
HW5: Implementation of K-Means Algorithm, and using this, implementing scene recognition with LibSVM, and various kernels.
HW6: Implementing Convolutional Neural Networks (CNN) for image classification and Recurrent Neural Networks (RNN) for action classification.