Joseph's class notes, in [PDF].

• Lecture 01: [PPT] : Overview; background on neural functioning
• Lecture 02: [PPT] : Neurons; motor control, mirror neurons
• Section notes, week 1: [PDF] [OpenOffice]: Neurons
• Lecture 03: [PPT] : Neural development
• Lecture 04: [PPT] : Connectionist models: basics
• Section notes, week 2: [PDF] [OpenOffice]: Reflexes, neural development, and neural modeling
• Lecture 05: [PPT] : Psychological considerations
• Lecture 06: Brain imaging. We have not yet received these slides
• Section notes, week 3: [PDF] [OpenOffice]
• Lecture 07: [PPT] : Learning: biological and connectionist
• Lecture 08: [PPT] : Backpropagation
• Section notes, week 4: Backpropagation and a little bit of learning [PDF] [OpenOffice]
• Lecture 09: [PPT] : Color vision and language
• Lecture 10: [PPT] : Representations and categories
• Section notes, week 5: [PDF] [OpenOffice] : Color, representations, and categories
• Lecture 11: [PPT] : Categories and Concepts
• Lecture 12: [PPT] Michael Ellsworth's guest lecture on FrameNet
• Section notes, week 6: [PDF] : Linguistics
• Lecture 13: [PPT] : Image schemas, including a story about learning them
• Lecture 14: [PPT] : Regier's model continued, plus review for midterm
• Section notes, week 7: [OpenOffice] [PDF] : Midterm review
• Lecture 15: [PPT] : X-Schemas and Petri Nets. Also see these Petri nets which can be viewed with PIPE2
• Section notes, week 8: [OpenOffice] [PDF] : X-Schemas and Actions (I also went over the midterm; it is not posted here.)
• Lecture 16: [PPT] : Best fit, parameterized actions, and model merging
• Lecture 17: [PPT] : The biology of reinforcement learning
• Section notes, week 9: [OpenOffice] [PDF] : Learning grammars via minimum description length
• Lecture 18: [PPT] : Reinforcement learning
• Lecture 19: [PPT] : Reinforcement learning: algorithms and further biology
• Section notes, week 10: [OpenOffice] [PDF] : Reinforcement learning
• Lecture 20: [PPT] : Event structure metaphor
• Lecture 21: [OpenOffice] [PDF] : Last year's slides on Bayes Nets
• Section notes, week 11: [OpenOffice] [PDF] : Review of metaphor, plus some Bayes Nets and a look at Leon's research
• Lecture 22: the karmaSIM metaphor model
• Lecture 23: [PPT] : Grammars and unification
• Section notes, week 12: [OpenOffice] [PDF] : Parsing and unification
• Lecture 24: notes [PDF] : Nate's presentation ECG
• Lecture 25: part 1 [PDF] part 2 [PPT] : Intro to learning grammars
• Section notes, week 13: [OpenOffice] [PDF] : ECG
• Lecture 26: [PPT] : ECG
• Lecture 27: [PPT] : Learning ECG
• Section notes, week 14: [OpenOffice] [PDF] : ECG Learning
• Final review notes: [OpenOffice] [PDF]
• Lecture 28: [PPT] : Binding problem and review of all material from class