Machine Learning Biases

Related lectures (31)

Page 2 of 4

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

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

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores binary classification by regression, decision functions, and various cost functions.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.