Summer 2025 (Weekly Meetings)
Summer study group on connectionism and neural networks. We will read chapters from the Parallel Distributed Processing (PDP) volumes and do hands-on coding of neural networks for cognitive science research.
- Ambar Narwal
- Hoyoung Doh
- Phillip Hegeman
- Antonio Monaco
- Pin Yang
| Week | Date & Time | Topic |
|---|---|---|
| 1 | 2025-05-09 @ 1:30 PM | Initial gathering |
| 2 | 2025-05-14 @ 11:00 AM | PDP Chapter 1: The Appeal of Parallel Distributed Processing |
| 3 | 2025-05-23 @ 10:30 AM | PDP Chapter 2: A General Framework for Parallel Distributed Processing |
| 4 | 2025-05-30 @ 10:30 AM | - COGS319 Lab 2: Neural Networks That Don't Learn - (Original Paper) McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological review, 88(5), 375. |
| 5 | 2025-06-06 @ 10:30 AM | PDP Chapter 3: Distributed Representations |
| 6 | 2025-06-13 @ 10:30 AM | COGS319 Lab 3: Training neural networks, overfitting, and regularization |
| 7 | 2025-06-20 @ 10:30 AM | Postponed |
| 8 | 2025-06-27 @ 10:30 AM | PDP Chapter 4: PDP Models and General Issues in Cognitive Science |
| 9 | 2025-07-04 @ 10:30 AM | Postponed |
| 10 | 2025-07-10 @ 10:30 AM | Recurrent Neural Netorks - (Textbook) ISLR Chapter 10.5. Recurrent Neural Networks - (Original paper) Elman, J. (1990). Finding structure in time. Cognitive Science, 14(2), 179-211. - (Implementation) COGS319 Lab 5: Finding structure in time with recurrent networks - (Video on RNNs and Transformers) The 35 Year History of LLMs |
| 11 | 2025-07-17 @ 10:30 AM | Backpropagation - PDP Chapter 8: Learning Internal Representations by Error Propagation - (Video) The Most Important Algorithm in Machine Learning - (Video) Backpropagation, intuitively | Deep Learning Chapter 3 |
| 12 | 2025-07-24 @ 10:30 AM | Convolutional Neural Networks - (Textbook) ISLR Chapter 10.3: Convolutional Neural Networks - (Implementation) COGS319 Lab 7: Introduction to CNNs - (Video with visualizations) The moment we stopped understanding AI [AlexNet] |
| 13 | - | No meeting (Conferences) |
| 14 | - | No meeting (Conferences) |
| 15 | 2025-08-15 @ 2:00 PM | Convolutional Neural Networks - COGS319 Lab 8: CNN features and categorizations |
| 16 | 2025-08-21 @ 10:30 AM | Hopfield Network - (Video) Hopfield network: How are memories stored in neural networks? - (Video) A Brain-Inspired Algorithm For Memory - (Original paper) Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558. - (Implementation) Code Example |
https://iu.zoom.us/j/87594458181
- Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition: Foundations (Vol. 1). MIT Press.
- McClelland, J. L., Rumelhart, D. E., & PDP Research Group. (1987). Parallel distributed processing: Explorations in the microstructure of cognition: Psychological and biological models (Vol. 2). MIT Press.
- [Code] COGS319 Labs & Solutions
-
Jurafsky, D., & Martin, J. H. Speech and Language Processing.
A comprehensive introduction to computational models of language, covering CNNs, RNNs, and Transformers. -
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R.
Introductory ML textbook; Chapter 10 covers neural networks with helpful visuals and math. -
[YouTube] Deep Learning & Machine Learning Playlist – Deeplizard
-
The 35 Year History of LLMs
The evolution of neural networks for language (i.e., RNNs and Transformers). -
The moment we stopped understanding AI [AlexNet]
The first CNN (i.e., AlexNet) explained with good visualizations. -
Hopfield network: How are memories stored in neural networks?
Hopfield networks as models of associative memory. Nobel Prize-winning concept in 2024. -
Neural Networks Playlist
Intuitive explanations about the math behind neural networks by 3b1b.