marija-stanojevic / ml-hw

Machine Learning - Homework solutions (Oct 2017)

Machine Learning Class - Homework solutions (October 2017)

These are solutions to homework 1 and homework 2 in machine learning class. Both of them required implementation of certain machine learning methods without using existing libraries except for basic functionalities and data structures.

Homework 1:

Uses Letter Recognition Data Set from the UCI Machine Learning (https://archive.ics.uci.edu/ml/datasets/letter+recognition). First 15,000 examples are for training and the remaining 5,000 for testing. Homework is implementing basic k-NN classification (test_knn method) and "pocket" algorithm (train_pocket and test_pocket methods). Additionally, method compute_accuracy computes the accuracy of methods results.

Homework 2:

Implements gradient descent (train_classifier method) for different cost functions (squared, hinge, logistic) without regularizer and with L1 and L2 regularizers. Classifiers can be crossvalidated for different parameters values (cross_validation method), tested using test_classifier method and evaluated using compute_accuracy method. System is tested on Wine Quality data set from the UCI Machine Learning (https://archive.ics.uci.edu/ml/datasets/wine+quality).