6.034 Artificial Intelligence - Recitations, fall 2004 online slides on learning S
	Machine Learning Common Definitions Categories of Learning A General Model of Learning Agents The Learning Problem Learning From Examples Prediction Problems Prediction Problems Prediction Problems Prediction Problems Prediction Problems Inductive Learning Hypothesis Inductive Bias Decision Trees Decision Tree Representation When to Consider Decision Trees Inducing Decision Trees Decision Tree Learning Top-Down Induction of Decision Trees Four Cases to Consider Which Attribute Is Best? Entropy Information Content Information Theory and Decision Trees Information Gain Training Examples Selecting the Next Attribute Partially Learned Tree Danger: Overfit Measure Performance of a Learning Algorithm Measure Performance of a Learning Algorithm Example Examples Neural Networks Power in Numbers Neuron A Simple Neural Network - The Perceptron Neuron Neural Networks Learn a Function Applications When to Consider Neural Networks Two Computation Phases Parameters That Can Affect Performance One Layer Net Code Example Example The AND Function Function Learned By One Layer Network Examples Linearly Separable How Can We Learn These Functions? A Perceptron Performs Gradient Descent Multilayer Neural Networks Function Learned by MNN Learning in a Multilayer Neural Network Delta Rule Hidden-to-output Weights Next Layer Example Update Function Step Function Sigmoid Function NN application - NETtalk Examples Networks That Deal With Time Neural Network Issues