10-601 Classification and K-NN
From Cohen Courses
Revision as of 09:35, 16 September 2014 by Wcohen (talk | contribs) (→What You Should Know Afterward)
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Ziv's lecture: Slides in pdf.
- William's lecture (draft): Slides in Powerpoint.
Readings
- Mitchell, Chapters 1,2 and 8.
What You Should Know Afterward
- What is the goal of classification
- What sorts of problems can be solved by reducing them to classification
- What the K-NN algorithm is.
- How the value of K affects the tendency of K-NN to overfit or underfit data.
- Optional:
- Bayes decision boundary for classification
- Is there an optimal classifier?
- What decision boundary is defined by K-NN, and how it compares to decision boundaries of linear classifiers.
- Probabilistic interpretation of KNN decisions
