Introduction to artificial intelligence focusing on supervised learning, nearest-neighbor classification, perceptron learning, support vector machines, regression, loss functions, overfitting, regularization, reinforcement learning, Markov decision processes, Q-learning, unsupervised learning, and k-means clustering.

#Supervised Learning#Nearest-Neighbor Classification#Perceptron Learning

January 1, 2026

Lecture 5: Neural Networks

Introduction to artificial intelligence focusing on artificial neural networks, activation functions, gradient descent, backpropagation, overfitting, TensorFlow, image convolution, convolutional neural networks, and recurrent neural networks.

#Artificial Neural Networks#Activation Functions#Gradient Descent