Machine-Learning-CE717

Spring 2021

Solution to practical homeworks and projects of Machine learning graduate course CE-717

1. EDA & Visualization:

This is a practice of EDA and Visualization methods on this dataset.

2. Linear and Polynomial Regression:

This is a practice of implementation Linear and Polynomial Regression from scratch in Python.

3. Logistic regression-decision tree-Adaboost:

A practice of three machine-learning methods on Blood Transfusion Service Center dataset

4. Loopy Belief:

This is an implementation of Loopy belief propagation for LCPD.

5. Kmeans & GMM:

This is an implementation of K-means and EM for GMM from scratch.

6. Pneumonia Classification with Resnet:

Resnet implementation for Chest-Xray Pneumonia Dataset.

7. Projects:

1) COVID19-data-classification-prediction:

A machine learning project to anticipate if patients need ICU to plan better during COVID-19 epidemy. I tested different methods. My final method is Logistic Regression which is able to predict if a patient needs to be admitted to ICU only by using the data in his first hours of being in the hospital. With this method I have reached to F-Score of 83%

2) Sarcasm Detection:

This is a group project in which we have tested many methods include SVM, Adaboost, Naive-Bayes and Deep-Learning Networks in order to detect whether a text is sarcastic or not. Our dataset is Reddit comments.