Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
HWs:
- Classification using KNN:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW1 - Regression:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW2 - Time Series Classification (part1):
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW3 - Time Series Classification (part2):
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW4 - Decision Trees as Interpretable Models & The LASSO and Boosting for Regression:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW5 - Tree-Based Methods:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW6 - Multi-class and Multi-Label Classification Using Support Vector Machines:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW7 - Supervised, Semi-Supervised, and Unsupervised Learning:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/HW8 - Transfer Learning for Video Classification:
https://github.com/ehsankhaligh/DSCI552-Machine-Learning-for-Data-Science/tree/main/Homwrorks/Project