Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
