Homeworks from the Machine Learning and Deep Learning master course at Polythecnic of Turin.
Study the wine dataset and use different classification techniques to label each element to the right class.
Algorithms used: Nearest Neighbors, Support Vector Machine with Linear and RBF kernerl.
Train a Convolution Neural Network for image classification using the Caltech-101 dataset.
Neural networks were trained using 2 techniques: training from scratch and using transfer learning, with the Alexnet pre-trained NN.
Extra tests were also performed with VGG16 and RESNET18 NNs as backbones.
The task is to implement DANN, a Domain Adaptation algorithm, on the PACS dataset using AlexNet NN.
PACS is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven classes: dog, elephant, giraffe, guitar, horse, house, person.
Tests were performed with normal training and domain adaptation training.
Extra tests were performed splitting the database in the different domains: train on the Photo dataset evaluate on Cartoon dataset, train it again on Photo dataset and evaluate it on Sketch dataset.
Each HW was graded to up to 2 point. I scored perfectly getting 6 points out of 6.