Dimensionality ReductionIntroduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.
Dimensionality Reduction: PCA & LDACovers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Understanding AutoencodersExplores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.