https://github.com/nemat-al/advance_machine_leanring_technologies
Tasks for Advanced Machine Learning Technologies Course @ ITMO University.
https://github.com/nemat-al/advance_machine_leanring_technologies
deep-learning image-quality-assessment interpretable-machine-learning lime machine
Last synced: 7 months ago
JSON representation
Tasks for Advanced Machine Learning Technologies Course @ ITMO University.
- Host: GitHub
- URL: https://github.com/nemat-al/advance_machine_leanring_technologies
- Owner: nemat-al
- Created: 2024-04-19T15:59:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-19T16:59:26.000Z (over 1 year ago)
- Last Synced: 2025-01-23T08:44:32.206Z (9 months ago)
- Topics: deep-learning, image-quality-assessment, interpretable-machine-learning, lime, machine
- Language: Jupyter Notebook
- Homepage:
- Size: 4.47 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Advance_Machine_Leanring_Technologies
Some of the tasks for Advanced Machine Learning Technologies Course at ITMO University.
----
## Index
1. [Interpretable ML](#interpretable-ml)
2. [Image Quality Assessment](#image-quality-assessment)
---
## [Interpretable ML](https://github.com/nemat-al/Advance_Machine_Leanring_Technologies/blob/main/Interpretable%20ML/AML_T02.ipynb)
The task is to analyze the feature importance in the dataset for making predictions by the trained model [LIME frameworks](https://github.com/marcotcr/lime). The datast is a set of labeled messages, a Naive Bayes model was trained to classify messages to: 'spam' or 'ham'. Then LIME was used to explain the prediction of the model over one test example.
## [Image Quality Assessment](https://github.com/nemat-al/Advance_Machine_Leanring_Technologies/blob/main/Image%20Quality%20Assessment/AMLT_Task_04.ipynb)
The file contains different non reference image quality assessments applied on a dataset of 20 images. The results are compared to my own ranking of the images by computing pearson correlation.