# The Linear Model II

The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent.

• ##### The Learning Problem
Yaser Abu-Mostafa

The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem.

• ##### Error and Noise
Yaser Abu-Mostafa

Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy.

• ##### Training Versus Testing
Yaser Abu-Mostafa

Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize?

• ##### Theory of Generalization
Yaser Abu-Mostafa

Theory of Generalization - How an infinite model can learn from a finite sample. The most important theoretical result in machine learning.

• ##### The VC Dimension
Yaser Abu-Mostafa

The VC Dimension - A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom.

Yaser Abu-Mostafa

Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves.

• ##### The Linear Model II
Yaser Abu-Mostafa

The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent.

• ##### Neural Networks
Yaser Abu-Mostafa

Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers.

• ##### Overfitting
Yaser Abu-Mostafa

Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise.

• ##### Regularization
Yaser Abu-Mostafa

Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay.

• ##### Validation
Yaser Abu-Mostafa

Validation - Taking a peek out of sample. Model selection and data contamination. Cross validation.

• ##### Support Vector Machines
Yaser Abu-Mostafa

Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one.

• ##### Kernel Methods
Yaser Abu-Mostafa

Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins.