Understanding 10 601 Machine Learning Spring 2015 Lecture 5
Exploring 10 601 Machine Learning Spring 2015 Lecture 5 reveals several interesting facts. Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 5
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: linear regression, logistic regression, gradient descent
- Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
- Lecture 5
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 5
Topics: Topics: Logistic regression and its relation to naive Bayes, gradient descent Topics: high-level overview of
Topics: deep learning, restricted Boltzmann machines, privacy in
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