QMSS-GR5074_Spring2022

KO | EN

gitlite — search

#python #ai #typescript #golang #machine-learning #linux #java #kubernetes #vue #pytorch #security #mcp #ios #open-source

QMSS-GR5074_Spring2022

(★ 18)

Class repository for GR5074 - Projects in Advanced Machine Learning (Spring 2022)

QMSS-GR5074_Spring2022 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# QMSS GR5074 - PROJECTS IN ADVANCED MACHINE LEARNING

<br/>

__Instructor:__ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  [__Marco Morales__](mailto:marco.morales@columbia.edu), Columbia University

__TAs:__ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[__Rachel Lee__](mailto:yl3751@tc.columbia.edu), Columbia University\
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[__Heinrich Peters__](mailto:heinrichpeterz@gmail.com), Columbia University
<br/>

This repository is a companion to the course [__Projects in Advanced Machine Learning__](https://vergil.registrar.columbia.edu/#/courses/QMSSG5074_001_2022_1) taught at the [__Quantitative Methods in the Social Sciences__](http://qmss.columbia.edu/) program over the Spring of 2022. It contains curated reference materials, slides and sample code. You can find the most updated version of the course syllabus [__here__](/syllabus/GR5074_Spring2022.pdf). Make sure to check regularly for updates.

### Resources

* __Textbooks:__ While there are no required textbooks for this course, you will find these books to be very useful in addition to the lectures and course readings:

* Fran&ccedil;ois Chollet. [__Deep Learning with Python__](https://www.manning.com/books/deep-learning-with-python-second-edition?gclid=EAIaIQobChMIpMGs9LWq9AIVGWKGCh3-ZAiREAAYASAAEgIOY_D_BwE). Manning Publications. Greenwich CT, 2017
	* Andreas C M&uuml;ller and Sara Guido. [__Introduction to Machine Learning with Python__](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/). O’Reilly Media, Boston, MA, 2016 [[e-version available through Columbia Libraries](https://web-p-ebscohost-com.ezproxy.cul.columbia.edu/ehost/detail/detail?vid=0&sid=717e7b82-5369-4bcb-9d5b-114cec870a9f%40redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=1361381&db=nlebk)]
	* Aur&eacute;lien G&eacute;ron. [__Hands-On Machine Learning with Scikit-Learn and TensorFlow__](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/). O’Reilly Media, Boston, MA, 2017 [[e-version available through Columbia Libraries](https://web-p-ebscohost-com.ezproxy.cul.columbia.edu/ehost/detail/detail?vid=0&sid=822ed5e4-5bc5-47b9-b514-a669eafda44f%40redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=1486117&db=nlebk)]
	* Max Kuhn & Kjell Johnson. [__Applied Predictive Modeling__](https://link.springer.com/book/10.1007/978-1-4614-6849-3). Springer, New York, NY, 2013 [[e-version available through Columbia Libraries](https://link-springer-com.ezproxy.cul.columbia.edu/book/10.1007%2F978-1-4614-6849-3)]
	* Trevor Hastie, Robert Tibshirani & Jerome Friedman. [__The Elements of Statistical Learning__](https://link.springer.com/book/10.1007/978-0-387-84858-7). Springer, New York, NY, 2009 [[e-version available through Trevor Hastie's site](https://hastie.su.domains/ElemStatLearn/)]
	* Ian Goodfellow, Joshua Bangio & Aaron Courvill. [__Deep Learning__](https://mitpress.mit.edu/books/deep-learning). MIT Press, Cambridge, MA, 2016 [[e-version available through Columbia Libraries](https://web-p-ebscohost-com.ezproxy.cul.columbia.edu/ehost/detail/detail?vid=0&sid=ae1fe930-c176-42a0-8117-f8ef78244076%40redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=2565107&db=nlebk)]

* __Cloud Services:__ The course will work in the cloud, and rely heavily on [__Colaboratory__](https://colab.research.google.com) and [__AI Model Share__](https://www.modelshare.org); registered students will receive instructions on access to these services. Sign up for a [__GitHub__](https://github.com) account if you don't already have one.

### Accessing course materials in this repo

1. install [**git**](https://git-scm.com/downloads) in your local machine

2. from the command line, go to the directory where you want to clone this repo

```
	$ cd <your chosen directory>
	```

3. `clone` this repository to get a local copy in your machine

```
	$ git clone https://github.com/marco-morales/QMSS-GR5074_Spring2022.git
	```

4. `pull` every week before class to sync your local copy with the lates changes pushed to the repo

```
	$ git pull origin main
	```

5. "Watch" the repository to get notifications each time uptates are pushed

__Acknowledgements__: Materials in this repository derive from previous iterations of this course taught by [**Mike Parrot**](https://github.com/mikedparrott).

QMSS-GR5074_Spring2022

QMSS-GR5074_Spring2022 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

QMSS-GR5074_Spring2022

QMSS-GR5074_Spring2022 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README