Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Related lectures (50)

Covers exercises on linear and weighted regression solutions, SVR problems, and Regular Least Squares.

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Covers feature selection, kernel regression, and neural networks through exercises.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Page 3 of 3