CS3920 Machine Learning Labs

Welcome to the repository for the CS3920 Machine Learning Labs. This series of labs is designed to provide a deep dive into the world of machine learning, exploring various algorithms, preprocessing techniques, optimization methods, and performance evaluation strategies.

Each lab corresponds to a week of study and practice, covering specific topics that build upon each other to enhance your understanding of machine learning concepts and applications.

Weekly Lab Topics

Week 1: Further Introductions and Nearest Neighbours

• In-depth introduction to the machine learning course.
• Fundamental machine learning algorithm - Nearest Neighbours.
• Introduction to Conformal Predictors.

Week 2: Introduction to Conformal Prediction

• Study of reliable prediction algorithms with guaranteed reliability.
• Delving into conformal prediction techniques.

Week 3: Conformal Prediction

• Detailed exploration of full conformal prediction.
• Application and analysis of conformal prediction algorithms.

Week 4: General Principles

• Understanding overfitting, underfitting, and the learning curve.
• Exploring the general principles of machine learning.

Week 5: Linear Regression

• Discussion of Least Squares.
• Improvement to Ridge Regression and introduction to Lasso.

Week 6: A Few Advanced Topics

• Data preprocessing and its impact on prediction quality.
• Parameter selection methods.
• Introduction to inductive conformal prediction.

Week 7: Kernels

• Enhancing linear methods with Kernels.
• Application of kernels to various machine learning methods.

Week 8: Two Powerful Algorithms

• Exploration of neural networks.
• Introduction to support vector machines.

Week 9: Two More Advanced Topics

• Understanding and creating pipelines.
• Efficient version of conformal prediction: Cross-conformal predictors.

Week 10: Finale

• Overview of a variety of new prediction algorithms.
• Broad introduction to advanced machine learning techniques.

Running Notebook Locally

These are simple steps to run the notebook locally. Jupyter is required.

1. Clone the Project Locally

git clone git@github.com:mbeps/Machine-Learning-Labs-Questions.git

2. Set Up Environment

Using Anaconda (Preferred)

If you have Anaconda installed, create a new environment and activate it. Once active, install the required packages.

Using Poetry (Alternative for non-Anaconda users)

1. Ensure you have Python 3.10 installed.
2. If you are using Poetry and do not have Anaconda, install the dependencies:

poetry install

3. Run the Project

After setting up the environment and installing all dependencies, navigate to the project's root directory and run the main notebook.

Note: Adjust the specific steps, commands, or any other requirements based on the nature of your project or any additional configurations that might be needed.