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 |  9/10 | <span class="label">Lecture 4</span> [Linear Regression 1](https://berkeley-haas.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=0b684900-3ef3-411e-bb68-b1d4013eff13) ([Slides](https://drive.google.com/file/d/14vblwqegBN7BC8-l1sAsRmiEVIu0TFmR/view?usp=drive_link)) | <span class="label label-purple">Reading</span> <nobr>4-4.1.4</nobr> |
 |  | <span class="label label-green">Discussion 1</span> MLE & Gaussians  | [Worksheet](docs/dis_fa24/dis1.pdf) / [Solutions](docs/dis_fa24/dis1sol.pdf) |
-|  9/12 | <span class="label">Lecture 5</span> Linear Regression 2  | <span class="label label-purple">Reading</span> <br> 1.25-1.26 (regularization, model selection) <br> 2.1.3 (Bayes theorem) <br> 2.6 (Bayesian modeling) <br> 4.1.6 (regularization for linear regression) <br> 9.2.2 (Lasso regularization) |
+|  9/12 | <span class="label">Lecture 5</span> [Linear Regression 2](https://berkeley-haas.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=56e9bd90-5e0c-4333-bbac-b1d4013eff2e) ([Slides](https://drive.google.com/file/d/1qCVrJIbVj8ypWQh8F5mIMCn0CD7yvMvU/view?usp=drive_link))  | <span class="label label-purple">Reading</span> <br> 1.25-1.26 (regularization, model selection) <br> 2.1.3 (Bayes theorem) <br> 2.6 (Bayesian modeling) <br> 4.1.6 (regularization for linear regression) <br> 9.2.2 (Lasso regularization) |
 |  | <span class="label label-yellow">Homework 2</span> Linear/Logistic Regression & Classification <nobr>(<strong>Due 9/25 11:59pm</strong>)</nobr>  | [PDF](docs/hw_fa24/hw2.pdf) / [Files](docs/hw_fa24/hw2.zip) / [LaTeX Template](docs/hw_fa24/hw2template.tex) |
 
 ### Week 4