classification-clustering-machine-learning

In this project, I solved several tasks on classification and clustering. There are two theoretical questions on K-means clustering and SVM. Additionally, there are two practical tasks on Ensemble Learning and CNN.

It also has a bonus task on GANs

Ensemble Learning

In this task, we will use some network layer features such as Duration, Number of packets, etc. to build a machine learning classification model that will detect Android malware applications, using app features. (More info in the problem_statement file)

CNN

In this task, we are going to implement CNN for calculating human iris center. This CNN architecture is proposed in https://ieeexplore.ieee.org/abstract/document/8803121 as a fully convolutional network which consists of a base network and auxiliary network. (More info in the problem_statement file)

GAN

In this task, we will generate Fake Faces using GANs.

Code

It can be found in the files named CNN.ipynb_ , Ensemble_Learning.ipynb , GAN.ipynb

Problem Statement

It can be found in the file named ML_A2.pdf

Theoretical questions

It can be found in the file named ML_A2_Theory.pdf

Datasets

All the necessary datasets have been provided with necessary links in the problem statement file

Run

To run the code:

1. Download the zip
2. Extract it
3. Use https://colab.research.google.com/?utm_source=scs-index
4. Upload the notebook and datasets
5. Run each cell

Results

Check the notebook with code, the cells were ran already and they have some output