10 601 Machine Learning Spring

Media Summary: Topics: review of the solutions to midterm exam Lecturer: Travis Dick Topics: review of boosting, Adaboost, strong vs weak PAC Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ...

10 601 Machine Learning Spring - Detailed Analysis & Overview

Topics: review of the solutions to midterm exam Lecturer: Travis Dick Topics: review of boosting, Adaboost, strong vs weak PAC Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ... Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ... Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ... Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ...

Topics: additional practice for graphical models, conditional independence, inference Lecturer: Micol Marchetti-Bowick ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...

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10-601 Machine Learning Spring 2015 - Recitation 10

10-601 Machine Learning Spring 2015 - Recitation 8

10-601 Machine Learning Spring 2015 - Lecture 1

10-601 Machine Learning Spring 2015 - Recitation 9

10-601 Machine Learning Spring 2015 - Recitation 4

10-601 Machine Learning Spring 2015 - Lecture 10

10-601 Machine Learning Spring 2015 - Lecture 23

10-601 Machine Learning Spring 2015 - Recitation 3

10-601 Machine Learning Spring 2015 - Lecture 3

10-601 Machine Learning Spring 2015 - Recitation 7

10-601 Machine Learning Spring 2015 - Recitation 5

10-601 Machine Learning Spring 2015 - Recitation 2

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10-601 Machine Learning Spring 2015 - Recitation 10

10-601 Machine Learning Spring 2015 - Recitation 10

Topics: support vector

10-601 Machine Learning Spring 2015 - Recitation 8

10-601 Machine Learning Spring 2015 - Recitation 8

Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.

10-601 Machine Learning Spring 2015 - Lecture 1

10-601 Machine Learning Spring 2015 - Lecture 1

Topics: high-level overview of

10-601 Machine Learning Spring 2015 - Recitation 9

10-601 Machine Learning Spring 2015 - Recitation 9

Topics: review of boosting, Adaboost, strong vs weak PAC

10-601 Machine Learning Spring 2015 - Recitation 4

10-601 Machine Learning Spring 2015 - Recitation 4

Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ...

10-601 Machine Learning Spring 2015 - Lecture 10

10-601 Machine Learning Spring 2015 - Lecture 10

Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...

10-601 Machine Learning Spring 2015 - Lecture 23

10-601 Machine Learning Spring 2015 - Lecture 23

Topics: never-ending

10-601 Machine Learning Spring 2015 - Recitation 3

10-601 Machine Learning Spring 2015 - Recitation 3

Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...

10-601 Machine Learning Spring 2015 - Lecture 3

10-601 Machine Learning Spring 2015 - Lecture 3

Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ...

10-601 Machine Learning Spring 2015 - Recitation 7

10-601 Machine Learning Spring 2015 - Recitation 7

Topics: additional practice for graphical models, conditional independence, inference Lecturer: Micol Marchetti-Bowick ...

10-601 Machine Learning Spring 2015 - Recitation 5

10-601 Machine Learning Spring 2015 - Recitation 5

Topics:

10-601 Machine Learning Spring 2015 - Recitation 2

10-601 Machine Learning Spring 2015 - Recitation 2

Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...

10-601 Machine Learning Spring 2015 - Recitation 11

10-601 Machine Learning Spring 2015 - Recitation 11

Topics: graph-based semi-supervised