Black Boys Code Durham- Types of Machine Learning

Workshop Overview

The October Technology Workshop will introduce students to the exciting world of machine learning, where computers can be trained to recognize patterns, make predictions, and solve problems. They will explore the two main types of machine learning - supervised learning, where models are trained with labeled examples, and unsupervised learning, where hidden patterns and structures in data are uncovered without labels.

Beginners will gain hands-on practice by grouping objects, spotting patterns, and training simple models, building a strong foundation in how machines “learn.”

Advanced learners will go further by working through real-world scenarios, experimenting with both supervised and unsupervised methods, analyzing data to select the right approach, and refining model performance through guided activities. By the end of the session, advanced students will have developed practical strategies for improving accuracy and efficiency, preparing them for more complex AI and data science challenges.

Beginner Track

Materials Required: Student PowerPoint

Beginner Track Learning Outcomes

• Differentiate between supervised and unsupervised learning.

• Group objects and identify patterns in data.

• Train a model with examples using supervised learning.

• Apply unsupervised learning by exploring hidden structures in data.

• Compare similarities between human learning and machine learning.

Advanced Track

Materials Required: Student PowerPoint

Advanced Track Learning Outcomes

• Apply supervised learning through guided training tasks.

• Experiment with unsupervised learning to find patterns.

• Analyze case studies to identify correct ML approaches.

• Explore the role of features, labels, and training data.

• Develop strategies to enhance model performance.

If you’re unsure who your Chapter Leader is, please contact to get connected and learn how you can support your local team.

Register Now, Space Is Limited!

Host or Publisher Black Boys Code