CS 330 - Artificial Intelligent - Logistic and linear regression Instructor: Renzhi Cao Computer Science Department Pacific Lutheran University Fall 2018 Special appreciation to Tom Mitchell, Ian Goodfellow, Joshua Bengio, Aaron Courville, Michael Nielsen, Andrew Ng, Katie Malone, Sebastian Thrun, Ethem Alpaydin, Christopher Bishop,
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CS 330 - Artificial Intelligentcaora/cs330/Materials/fall2018/Slides/Day13.pdf · CS 330 - Artificial Intelligent - Logistic and linear regression Instructor: Renzhi Cao Computer
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CS 330 - Artificial Intelligent - Logistic and linear regression
Instructor: Renzhi Cao Computer Science Department
Pacific Lutheran University Fall 2018
1
Special appreciation to Tom Mitchell, Ian Goodfellow, Joshua Bengio, Aaron Courville, Michael Nielsen, Andrew Ng, Katie Malone, Sebastian Thrun, Ethem Alpaydin, Christopher Bishop,
Announcement
• Homework of decision tree is due on next Tuesday • Lab 3 is due on Sakai • Quiz on next week, study guide will be posted on Sakai • Practical Machine learning next Tuesday, bring your laptop
Gaussian Naive Bayes - Big Picture
Logistic Regression
Idea: • Naive Bayes allows computing P(Y|X) by learning P(Y) and
1. MLE corresponds to minimizing sum of squared prediction errors
2. MAP estimate minimizes SSE plus sum of squared weights
3. Again, learning is an optimization problem once we choose our objective function• maximize data likelihood• maximize posterior prob of W
4. Again, we can use gradient descent as a general learning algorithm• as long as our objective fn is differentiable wrt W• though we might learn local optima ins
5. Almost nothing we said here required that f(x) be linear in x