Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.
