{"id":18513572,"url":"https://github.com/marko19907/ml-assignments","last_synced_at":"2026-04-29T12:32:31.360Z","repository":{"id":103773738,"uuid":"581365562","full_name":"Marko19907/ML-assignments","owner":"Marko19907","description":"Machine Learning assignments, Machine Learning (IE500618) course, fall 2022.","archived":false,"fork":false,"pushed_at":"2022-12-25T14:11:18.000Z","size":296,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-14T12:57:08.408Z","etag":null,"topics":["cifar100","confusion-matrix","distributed-machine-learning","ensamble-methods","jyputer-notebook","machine-learning","mnist-dataset","multilayer-perceptron-network","mushroom-classification","python","resnet-50","transfer-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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contains the mandatory assignments from NTNU's \"Machine Learning\" (IE500618) course, fall 2022. \n\nThese assignments are mandatory but do not count towards the final grade in the subject.\n\n\n## Contents\n\n### [A1: Mushroom Classification](/A1-Mushroom-Classification)\n* [x] Use the [UCI Mushroom data set](https://archive.ics.uci.edu/ml/datasets/mushroom)\n* [x] Use a multilayer perceptron (MLP) classifier.\n* [x] Clean and split the data into training, validation, and testing.\n* [x] Present the results:\n    * [x] Plot the accuracy and loss.\n    * [x] Create a confusion matrix.\n\n### [A2: Distributed ML](/A2-Distributed-ML)\n* [x] Simulate distributed machine learning using ensemble learning and compare it to a monolithic model.\n* [x] Use the [MNIST data set](http://yann.lecun.com/exdb/mnist/)\n* [x] Use a multilayer perceptron (MLP) classifier.\n* [x] For the ensemble model:\n  * [x] Divide the data into 3 local sections, by digits: 0-2, 3-5, and 5-9.\n  * [x] Train each local model with only one of the sections.\n  * [x] Aggregate the 3 local models into a single ensemble model.\n* [x] Present the results:\n  * [x] Plot the accuracy and loss.\n  * [x] Create a confusion matrix.\n  * [x] Make comparisons between the ensemble model and the monolithic model trained on the full dataset.\n\n### [A3: ResNet50 (transfer learning) with CIFAR100](/A3-ResNet50-(transfer-learning)-with-CIFAR100)\n* [x] Use the ResNet50 model (transfer learning) for classification.\n* [x] Use the [CIFAR-100 data set](https://www.cs.toronto.edu/~kriz/cifar.html)\n* [x] Present the results:\n  * [x] Plot the accuracy and loss.\n  * [x] Create a confusion matrix.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarko19907%2Fml-assignments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarko19907%2Fml-assignments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarko19907%2Fml-assignments/lists"}