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Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.
Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.
Explores quantum chemistry applications, emphasizing the role of electron density in predicting chemical properties and addressing challenges in catalyst design, solar energy conversion, and drug synthesis.
Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.
