| # | Topic | Description |
|---|---|---|
| 4.1 | Introduction to Artificial Neural Networks | Biological motivation, ANN representations, ALVINN example |
| 4.2 | Appropriate Problems for NN Learning | When to use ANNs — 6 key problem characteristics |
| 4.3 | Perceptrons | Perceptron model, representational power, training rule, delta rule, gradient descent |
| 4.4 | Multilayer Networks and Backpropagation Algorithm | Sigmoid units, Backpropagation algorithm, momentum, derivation |
| 4.5 | Remarks on Backpropagation Algorithm | Convergence, local minima, representational power, hidden representations, overfitting |
| 4.6 | An Example: Face Recognition | Full design walkthrough — input encoding, network structure, learned representations |
| 4.7 | Advanced Topics of ANN — Error Functions | Weight decay, derivative errors, cross-entropy, weight sharing, conjugate gradient |
| 4.8 | Recurrent Networks | Time series, recurrent architecture, unfolding in time, dynamic network structure |
Source: Tom M. Mitchell, Machine Learning, Chapter 4
📄 Full Chapter PDF
← Unit 3: Introduction to Machine Learning
→ Unit 5: Decision Trees & Reinforcement Learning
PAIML Course Overview