Every Friday, we get together (over pizza, sometimes) for lab teachings. On a rotating basis, each member of the lab speaks and teaches about something they know. Anything, really. Relevant and interesting topics, good skills to know, nice Python packages, neuroscientific princples, new findings and literature reviews… whatever!
Get on the listserve for announcements: https://groups.google.com/forum/#!forum/kording-lab-teachings
Spring 2020 topics
| Date | Name | Topic |
|---|---|---|
| Jan. 15 | Ari | Variational Inference |
| (Week of) Jan. 20 | Roozbeh | Why overparameterized deep networks generalize well? |
| (Week of) Jan. 27 | ||
| (Week of) Feb. 3 | ||
| (Week of) Feb. 10 | ||
| (Week of) Feb. 17 | ||
| (Week of) Feb. 24 | Tony | TBA |
| (Week of) Mar. 2 | Nidhi | TBA |
| (Week of) Mar. 9 | Titipat | Reinforcement Learning (policy based, actor-critic, …) - continue |
| (Week of) Mar. 16 | Ben | TBD |
| (Week of) Mar. 23 | ||
| (Week of) Mar. 30 | ||
| (Week of) Apr. 6 | ||
| (Week of) Apr. 13 | ||
| (Week of) Apr. 20 | ||
| (Week of) Apr. 27 | ||
| (Week of) May 4 | ||
| (Week of) May 11 |
Requests and suggestions
Recently taught topics
For inspiration. Add ones you’ve done!!
Fall 2019
| Date | Name | Topic |
|---|---|---|
| Sept. 28 | Ilenna | Capacity of Neural Networks |
| Oct. 5 | Tung Pham | GANs for EEG |
| Oct. 12 | Ben | GPUs – beneath the heatsink Slides |
| Oct. 19 | Rachit | Graph Convolution Networks |
| Oct. 26 | Tony | Docker for science |
| Nov. 2 | Titipat | AllenNLP library and a little bit of Pytorch |
| Nov. 9 | Roozbeh | Multiple Hypothesis Testing |
| Nov. 16 | David | Reinforcement learning and catastrophic forgetting |
| Dec. 3 | Ari | Independent Component Analysis |
| Jan. 9 | Netanel Ofer | Automated Analysis of Interneuron Axonal Tree Morphology and Activity Patterns |
| Jan. 18 | Nidhi | Dynamic Time Warping |
| Jan. 25 | Ben | Bandit problems |
| Feb. 11 | David | Autoencoders & Information Bottleneck |
| Feb. 27 | Adrian Radillo | Perfecting the research process [dropbox doc from the teaching] (https://paper.dropbox.com/doc/Kordings-lab-teaching-on-IT-for-scientists–AYUMIhaJvifuArh1uCfm6BivAQ-wXXjZyfix7HiGu9lcroyR) |
| Mar. 6 | Ari | Biologically plausible backprop |
| Mar. 13 | Greg Corder (http://www.corderlab.com/) | emotional processing of pain in the amygdala |
| Mar. 20 | Ilenna | Topics in the Philosophy of Science |
| Mar. 27 | Tony | Code Workflow for Research |
| May 1 | Edgar Dobriban | Data augmentation |
| May 15 | Ben Baker (Miracchi lab) | Representation and information in neuroscience |
| May 29 | Sebastien Tremblay (Platt Lab) | The limits of neurophys and why we need your help |
| June 5 | Zhihao (Princeton University) | TBA |
| October 9 | David Rolnick | Climate change |
| October 16 | Ari Benjamin | TBD (plasticity & learning in the brain) |
| October 23 | Ethan Blackwood | Neural models of indirection and abstraction |
| October 30 | Ben Lansdell | Invariance and causality |
| November 6 | Nidhi Seethapathi | Inferring Dynamics from Data |
| November 13 | Tony Liu | Theory of Computation |
| November 20 | Ilenna Jones | Ion Channel Kinetics |
| Mar. 27 | Tony | Code Workflow for Research |
| May 1 | Edgar Dobriban | Data augmentation |
| May 15 | Ben Baker (Miracchi lab) | Representation and information in neuroscience |
| May 29 | Sebastien Tremblay (Platt Lab) | The limits of neurophys and why we need your help |
| June 5 | Zhihao (Princeton University) | TBA |
| October 9 | David Rolnick | Climate change |
| October 16 | Ari Benjamin | TBD (plasticity & learning in the brain) |
| October 23 | Ethan Blackwood | Neural models of indirection and abstraction |
| October 30 | Ben Lansdell | Invariance and causality |
| November 6 | Nidhi Seethapathi | Inferring Dynamics from Data |
| November 13 | Tony Liu | Theory of Computation |
| November 20 | Ilenna Jones | Ion Channel Kinetics |
| November 27 | Shaofei Wang | Differentiable Structured Inference and Attention |
| December 4 | Rachit Saluja | Compressed sensing and deep learning |
| December 18 | Titipat Achakulvisut | Reinforcement Learning (introduction) |
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