Lecture Topics - CN550 Spring 2008
- Lecture 1, Jan 28 - Overview, history, philosophy
- Lecture 2, Feb 4 - Supervised learning: Memory–based algorithms (KNN), statistical pattern recognition, model-independent supervised learning methods (validation & cross-validation, c-index, ROC curves, resampling, combining classifiers, component analysis)
- Lecture 3, Feb 11 - Unsupervised learning: Clustering (leader, K-means), competitive learning, ART
- Lecture 4, Feb 19 - Dimensional analysis, competitive networks, phase plane analysis
- Lecture 5, Feb 25 - ARTMAP
- Lecture 6, March 3 - Associative memory networks: Back propagation, multi-layer perceptrons, radial basis functions, Cascade-correlation, higher-order networks
- Lecture 7, March 17 - Support vector machines
- Mar 24 - Mid-term exam (1-3 pm) + Movie: Memento
- Lecture 8, March 31 - Physiology, psychology, and memory models
- Lecture 9, Apr 7 - Content-addressable memories (CAM), active network design
- April 11 - Roger Ratcliff, CELEST Science of Learning Seminar 2 pm
- Lecture 10, Apr 14 - Liapunov functions, Cohen–Grossberg theorem
- Lecture 11, Apr 23 - Three-layer feedforward networks: Theory and mathematical foundations
- Lecture 12, Apr 28 - Synapses, signal functions, distributed vs. winner-take-all coding. Course evaluations, class party.
- Lecture 13, Extra - Invariance, spatial preprocessing, oscillations, temporal order information (TOI).
- May 12 - Final exam (1:00 – 3:30 PM)