Week 2: Supervised Learning
Announcements
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Class participation, EdSTEM, Figma, and Google Folder.
Tuesday
Recap Notes last week
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Highlight the patterns your recognized
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Hands-on in-class exercises
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Revisit Lab 1 requirements
Pattern Recognition
Narrow down to course introduction, Identify Patterns.
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Textbooks
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Topics
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Weeks
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Resources
Let’s look at course examples.
Textbooks
All textbooks are free available online, and are optinal, not required.
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Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
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2016, Cited by 42286
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A Course in Machine Learning by Hal Daume III
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1997, Cited by 33979
In-class exercises:
*Explain to your classmates what you find interesting from one of the textbook *Explain to your classmates your experience with Google Scholar *Explain to your classmates one interesting resource you saw in the Resources from the courses.
Supervised learning
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5 Machine Learning Basics
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5.7. supervised, PDF page number 137
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5.8, unsupervised
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15, unsupervised learning
In-class exercises:
*Explain to your classmates what is supervised learning *Explain to your classmates what is unsupervised learning
Lab 02
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Supervised learning Basics
Thursday
Videos (long, hours)
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Stanford, Andrew Ng, and slides.
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CMU, Tom Mitchell, both videos and slides.
Videos (short, minutes)
In-class exercises:
*Explain to your classmates what is an interesting topic your saw from these videos.
AAAI Accepted Posters (2022)
AAAI Accepted Posters (2021)
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AAAI-21 Student Papers and Demonstrations
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High School
AAAI Accepted Posters (2020)
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Pattern Recognition from Accepted Posters
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Practice: Three minutes per poster, all forty posters together, around two hours.
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AAAI-20 Student Papers and Demonstrations
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High School
Supervised vs Unsupervised
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The main difference between supervised and unsupervised learning: Labeled data
Datasets
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Popular datasets, UCI Datasets
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Other institutions also use these datasets.
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sklearn datasets examples
Iris Dataset
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Programming Competitions use it as well, such as Kaggle.
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Videos, such as Classification with Iris Dataset.