Exploring 10 601 Machine Learning Spring 2015 Lecture 2
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Lecture 2.
- Topics: support vector
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
- Topics: inference in graphical models, d-separation, conditional independence
- Topics: exam review, review of past exam questions
- Topics: clustering, k-means, k-means++, hierarchical clustering
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 2
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Linear Algebra Topics: boosting, weak vs strong PAC
Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension
In summary, understanding 10 601 Machine Learning Spring 2015 Lecture 2 gives us a better perspective.
