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README.md

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-# BI-BE-CS-183-2023
-Website for the 2022-2023 Caltech class Bi/BE/CS 183: Introduction to Computational Biology and Bioinformatics
+## Caltech BI/BE/CSS 183: Introduction to Computational Biology and Bioinformatics
+This repository contains lecture notes, problem sets, and associated Google Colaboratory notebooks for the Caltech Bi/BE/CS 183 class taught by Professor [Lior Pachter](https://www.bbe.caltech.edu/people/lior-s-pachter) in the Winter Quarter 2022-2023. The TAs for the class were Tara Chari, Meichen Fang, and Jerry Wang. Along with tutoring students, the TAs helped prepare the materias and write the problem sets with solutions. An overview of the class is provided in the [syllabus](https://docs.google.com/document/d/1LV7rLCGQwl5F8pxuvCw_G8cntIuZRlpN7UFfKLJz2bc/edit?usp=sharing).
 
-Professor: Lior Pachter
-
-TAs: Tara Chari, Meichen Fang, and Jerry Wang. 
-
-All the materials for each of the homework assignments are provided in the individual folders. This includes the problem set pdf, Colab notebooks, and links to the relevant Lecture slides.
+### Lectures slides
+- [Lecture 1](https://docs.google.com/presentation/d/1-Bo7yaaaxbf8ul_2gacZIII_pggd6JmSVXEF4Zwqekg/edit?usp=sharing): Introduction to computational biology of single-cell RNA-seq
+- [Lecture 2](https://docs.google.com/presentation/d/1tpeNHSONBunT7TwZSlwm42VknPVNeufdY7A4rzqMhkI/edit?usp=drive_link): Single-cell RNA-seq technologies
+- [Lecture 3](https://docs.google.com/presentation/d/1P2tFP82zIwZHOlRQWu8qlFUjCck3BDhtTPquZssciio/edit?usp=drive_link): Linear and logistic regression
+- [Lecture 4](https://docs.google.com/presentation/d/1ZJQQSgKdQA7PUw2HyZmgJwRN1IgRXA0e1XTmL9D3iF8/edit?usp=drive_link): Correlation and causation
+- [Lecture 5](https://docs.google.com/presentation/d/1A0g8BbgGmQllI5y1kdEYOPRQ0Q9CPMDMmX-RQwbhRzM/edit?usp=drive_link): Singular value decomposition
+- [Lecture 6](https://docs.google.com/presentation/d/1DTuLMODtcFy-x1X1J4p7hy8aIxr9VnGyY98XNKs1B78/edit?usp=drive_link): Dimensionality reduction
+- [Lecture 7](https://docs.google.com/presentation/d/1hH6WcVqrTsZRJmkTstJNjIka5g2eJcCnD_sBn4G-J6w/edit?usp=drive_link): Clustering
+- [Lecture 8](https://docs.google.com/presentation/d/1G4s2D-Y2Z5LFVe41enVBepyffSAjuAhrJZ7Hzn8A3nU/edit?usp=drive_link): Read alignment
+- [Lecture 9](https://docs.google.com/presentation/d/1XELEyVhr0vMpkaGXq4INhNq8mXttqz_NZ1ZGclUFM0Y/edit?usp=drive_link): String algorithms
+- [Lecture 10](https://docs.google.com/presentation/d/1XjHXsOVMdO0DgP8NvNCHC82DyudHrtf2YXOzRoPBN5g/edit?usp=drive_link): Modeling counts
+- [Lecture 11](https://docs.google.com/presentation/d/1Snj90kIe6iguVfftZ18404C4h8WcHoEdA8isfy021GQ/edit?usp=drive_link): Generalized linear models
+- [Lecture 12](https://docs.google.com/presentation/d/1qTSqWCfXNwKxpMT5VcgCrv_gmZ9xlXazukaWjWYoJ3o/edit?usp=drive_link): Variance stablization
+- [Lecutre 13](https://docs.google.com/presentation/d/1ExkNVQ8u8IuZ1ZmUpTyd7oBuRkqU5RZEHm1q4eqK9Uc/edit?usp=drive_link): Differential analysis
+- [Lecture 14](https://docs.google.com/presentation/d/1F-OFNeVNClOYxTnJGW-pZ0KHzTIggPZeyZsk7y9gM0g/edit?usp=drive_link): Multiple testing
+- [Lecture 15](https://docs.google.com/presentation/d/1F-OFNeVNClOYxTnJGW-pZ0KHzTIggPZeyZsk7y9gM0g/edit?usp=drive_link): Hidden Markov models
+- [Lecture 16](https://docs.google.com/presentation/d/1ry13HMq3z-DERtXNnm5L_YHXQFtIltmoGDTq3zydn6k/edit?usp=drive_link): Dynamic programming
+- [Lecture 17](https://docs.google.com/presentation/d/1Nf528xSOGWQS6sEkkAdA1MSt4p7u3aHnttA-tXGlzP0/edit?usp=drive_link): Mechanism
+- [Lecture 18](https://docs.google.com/presentation/d/15lw0UxeSoVwxrmn8L5BYu_xr7ccMB2u3QvGcn7M0sa8/edit?usp=drive_link): The Chemical Master Equation
+- [Lecture 19](https://docs.google.com/presentation/d/1OroKl_6AyrX422RGkodctorMA1dBhNBwbITg_kzxB3Q/edit?usp=drive_link): Neural networks