This repository contains all the assignments for the AI course "Applications of Machine Learning" offered at McMaster University. Each assignment is designed to reinforce the concepts learned in class and provide hands-on experience with various machine learning techniques.
This course aims at equipping students with the knowledge and skills necessary for understanding and implementing various machine learning techniques.
"Overview of data engineering. Human-computation architectures. Supervised approaches (neural networks). Unsupervised approaches (clustering/topic modelling). Human-in-the-loop approaches (reinforcement learning)."
The course description provided in this repository is sourced from: COMPSCI 4AL3 - Applications of Machine Learning | McMaster University
Each folder contains the code for the assignemnent, as well as a requirements.txt file and a README.md file. The requirements.txt aims at easiying the process of installation. The README.md aims at providing explainations about the assignement and provide the link to the dataset used.
There are four assignments, each building upon concepts covered in the lectures. For more detailed information, please refer to the respective README files for each assignment.
- Gradient Descent, Linear and Polynomial Regression
- Support Vector Machines (SVM) for Classification and Cross-Validation
- Training Methods, Regularization, and Active Learning
- Convolutional Neural Networks (CNNs), Fairness and Bias
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Install Python: The version used for these assignement was version 3.10.12, but it might work with other versions.
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Create a Virtual Environment:
python -m venv venv
source venv/bin/activate
# on Windows use venv\Scripts\activate- Install Dependencies:
pip install -r requirements.txtFor Detailed Instructions on Running the Code, refer to the assignment-specific README files.