Winter 2021-22 semester, done by Dorin Shteyman and Ido Amit.
all HWs instructions are attached to this repository.
general description of the assignments:
HW1- Here we were presented with the Virus Test Challenge Dataset (VTC) containing labeled information from patients suffering from all sorts of diseases. Our goal was to understand this dataset and prepare it for prediction (featyure selection)
HW2- In this assignment we implemented two algorithms that we learned: k-NN and Soft-SVM. Moreover, we practiced basic hyperparameter tuning (model selection) using three algorithms: k-NN, ID3, and Soft-SVM.
HW3- In this final assignment, we tried to predict a continuous label using our dataset. Scientists have developed an advanced test that computes a probability that a patient is infected with the COVID virus. Unfortunately, this test is costly and cannot be performed at large scale. Our goal was to regress the outcome of this test, i.e., the “virus score”, with the cheap features we used in the previous assignments.