Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Machine Learning FundamentalsCovers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Reinforcement Learning ConceptsCovers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.