Advanced Machine Learning Course (NUP)

A comprehensive course on advanced machine learning techniques. This course provides hands-on experience with various unsupervised learning algorithms and other methods through practical assignments with automated testing.

Course Overview

This assignment covers advanced topics in machine learning, with a particular focus on:

• Data Aggregation and Processing
• Clustering Algorithms
• Expectation-Maximization (EM) Algorithm
• Semi-supervised Learning
• Topic Modeling

Course Structure

The course is organized into several modules under the Unsupervised learning section:

1. Intro: Introduction to unsupervised learning concepts
2. AggregationPoints10: Techniques for data aggregation including:
   • Majority voting
   • Dawid-Skene method
3. ClusterizationPoints20: Advanced clustering algorithms
4. EMforDSPoints30: Expectation-Maximization algorithm for Data Science
5. SemisupervisedPoints20: Semi-supervised learning techniques
6. TopicModelingPoints20: Topic modeling and text analysis

Usage

Each module contains:

• Task description (task.md)
• Implementation file (task.py or main.py)
• Test suite (tests/test_task.py)
• Additional resources when needed

To complete a task:

1. Read the task description in the respective module's task.md
2. Implement the required functionality in the implementation file
3. Run the tests to verify your solution

Testing

The course uses unittest framework for automated testing. To run tests for a specific task:

python -m unittest Unsupervised/ModuleName/tests/test_task.py

Author

Aleksandr Avdiushenko