This repository contains the mandatory assignments from NTNU's "Machine Learning" (IE500618) course, fall 2022.

These assignments are mandatory but do not count towards the final grade in the subject.

• Use the UCI Mushroom data set
• Use a multilayer perceptron (MLP) classifier.
• Clean and split the data into training, validation, and testing.
• Present the results:
  • Plot the accuracy and loss.
  • Create a confusion matrix.

• Simulate distributed machine learning using ensemble learning and compare it to a monolithic model.
• Use the MNIST data set
• Use a multilayer perceptron (MLP) classifier.
• For the ensemble model:
  • Divide the data into 3 local sections, by digits: 0-2, 3-5, and 5-9.
  • Train each local model with only one of the sections.
  • Aggregate the 3 local models into a single ensemble model.
• Present the results:
  • Plot the accuracy and loss.
  • Create a confusion matrix.
  • Make comparisons between the ensemble model and the monolithic model trained on the full dataset.

• Use the ResNet50 model (transfer learning) for classification.
• Use the CIFAR-100 data set
• Present the results:
  • Plot the accuracy and loss.
  • Create a confusion matrix.