Machine Learning: Basics and Applications

Related lectures (31)

Page 3 of 4

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

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

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.