Lectures related to Active learning (machine learning)

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

Covers Hebbian learning, reinforcement learning, types of learning, neuron models, learning rules, and weight homeostasis.

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

Explores discovering composable modules in meta-learning, focusing on flexible machine learning frameworks and the role of inductive biases.

Explores decentralized optimization in machine learning, emphasizing robustness, privacy, and fairness in collaborative learning.