Caltech-Machine-Learning-Course---CS-156

---

Homework 1

• The Learning Problem
• Bins and Marbles
• Feasibility of Learning
• The Perceptron Learning Algorithm

---

Homework 2

• Hoeffding Inequality
• Error and Noise
• Linear Regression
• Nonlinear Transformation

---

Homework 3

• Generalization Error
• Break Point
• Growth Function
• Fun with Intervals
• Convex Sets: The Triangle
• Non-Convex Sets: Concentric Circles

---

Homework 4

• Generalization Error
• Bias and Variance
• VC Dimension

---

Homework 5

• Linear Regression Error
• Nonlinear Transforms
• Gradient Descent
• Logistic Regression
• PLA as SGD

---

Homework 6

• Overfitting and Deterministic Noise
• Regularization with Weight Decay
• Regularization for Polynomials
• Neural Networks

---

Homework 7

• Validation
• Validation Bias
• Cross Validation
• PLA vs. SVM

---