Support Vector Machines: Non-Linear Data Mapping

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Page 3 of 3

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers non-convex optimization techniques and their applications in machine learning.

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.