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Introduction to Algorithms
Video 1: Open
List experiences
Ch 2 title
Video 2: Algos are like recipes
What are algos? (10 points)
Video 3: Machine learning
What is training data? (10 points)
Video 4: Black box problem
Explain training data and bias (25 points)
Video 5: How algos work
How do search algos work? (10 points)
Video 6: Bias in search algos
Which search least biased? (10 points)
Ch 3 title
Video 7: The attention economy
Reorder attention economy (10 points)
Video 8: Engagement
Costs of attention economy (10 points)
Explain two-way process (25 points)
Ch 4 title
Video 9: Celebrity clones
What can you do to reduce? (10 points)
Video 10: Filter bubbles
Match filter bubble and rabbit hole (10 points)
Feedback, filter, rabbit (25 points)
Video 11: Amplifying bias
Bias from training data (10 points)
Ch 5 title
Video 12: Introducing Gen
Why gen inaccuracies? (10 points)
Video 13: AI image generators
Why AI images stereotypes? (10 points)
Video 14: Other generative AI developments
AI image feedback algo bias (10 points)
Argue the risks and benefits (25 points)
Ch 6 title
Video 15 conclusion
List ways algo advantage
If you were asked to make a plan (10 points)
Acknowledgments
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In the last segment, Mia said that suggestion algorithms don’t just get to know you; they also change you.
What did she mean by this?
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