machine learning algorithms
Session 1 Activity
This activity is designed to introduce you to the concepts of machine learning algorithms in a familiar context, helping you grasp their basic principles and applications.
Scenario: You are a movie buff and want to recommend movies to your friends based on their preferences.
Machine Learning Algorithms in Action:
Supervised Learning: Imagine you have a dataset containing information about movies (genre, director, actors, etc.) and user ratings. You can use a supervised learning algorithm like a decision tree or k-nearest neighbors to analyze this data.
Activity: Based on sample movie data (you can create a simple table or use online resources), manually predict movie recommendations for a friend using their past ratings and the movie information. This simulates how a supervised learning algorithm makes predictions based on labeled data.
Unsupervised Learning: Now, let’s say you have user data with movie watch history but no ratings. You can use unsupervised learning like k-means clustering to identify groups (clusters) of users with similar movie preferences.
Activity: Group a list of movies from different genres into categories based on your own perception of what genres might be watched together. This is like how unsupervised learning finds hidden patterns in unlabeled data.
Recommendation Systems: Many streaming services use a combination of supervised and unsupervised learning to recommend movies to users.
- Activity: Reflect on the movie recommendations you receive on streaming platforms. What factors do you think influence these recommendations? How might supervised and unsupervised learning be used in these systems?
Discussion Points:
What are the strengths and weaknesses of each approach (supervised vs unsupervised) for movie recommendations?
- How can machine learning algorithms be biased? Consider potential biases in movie recommendations.
What other applications of machine learning algorithms can you think of in everyday life (e.g., social media feeds, online shopping)?
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