Mar. 19. Slides for today's lectures on Neural Networks (and the code) are available here.
Mar. 12. Lecture notes on neural networks and backpropagation have been uploaded. Please read these materials before the lectures.
Mar. 12. Slides for today's lectures on decision trees and random forest have been uploaded. Please also play with the code underfitting and overfitting.
Mar. 5. Lecture notes on decision trees and random forests and data, features, and evaluation are available. You're highly encouraged to read them before the lectures.
Mar. 5. Slides for today's lectures (on SVM) are available here. The second assignment (A2) has also started.
Feb. 26. Lecture notes on support vector machine are available. You're highly encouraged to read them before the lectures.
Feb. 26. Slides for lectures Bayesian classification and logistic regression have been uploaded.
Feb. 19. Lecture notes on Bayesian classification and logistic regression are available. You are encouraged to read these materials before the lectures.
Feb. 19. Slides for lectures clustering and neareast neighbor classification have been uploaded.
Feb. 12. Lecture notes on clustering and nearest neighbor classification are available. You're highly encouraged to read these materials before the lectures.
Feb. 12. The first assignment (i.e., A1: linear regression) is available on BrightSpace under 'Assignments'. Note: you must join a group to access the assignment.
Feb. 12. Slides and letcutre notes for today's lectures on introduction to machine learning and linear regression and gradient descent are available. Example code for gradient descent can be found here.
Feb. 2. The course website is online.
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