https://github.com/sylvaticus/mitx_6.86x
Notes of MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning
https://github.com/sylvaticus/mitx_6.86x
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Notes of MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning
- Host: GitHub
- URL: https://github.com/sylvaticus/mitx_6.86x
- Owner: sylvaticus
- License: mit
- Created: 2020-03-03T09:16:26.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-12-13T16:27:48.000Z (about 2 years ago)
- Last Synced: 2025-05-20T00:11:29.018Z (8 months ago)
- Language: HTML
- Homepage:
- Size: 65.5 MB
- Stars: 318
- Watchers: 18
- Forks: 141
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MITx_6.86x - Machine Learning with Python: from Linear Models to Deep Learning
https://www.edx.org/course/machine-learning-with-python-from-linear-models-to
Lecturers: [Regina Barzilay](https://www.edx.org/bio/regina-barzilay), [Tommi Jaakkola](https://www.edx.org/bio/tommi-jaakkola), [Karene Chu](https://www.edx.org/bio/karene-chu)
## Student's notes (2020 run) ##
_Disclaimer: The following notes are a mesh of my own notes, selected transcripts, some useful forum threads and various course material. I do not claim any authorship of these notes, but at the same time any error could well be arising from my own interpretation of the material._
**Contributions are really welcome**. If you spot an error, want to specify something in a better way (English is not my primary language), add material or just have comments, you can clone, make your edits and make a pull request (preferred) or just open an issue.
(PDF versions may be slightly outdated)
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For an implementation of the algorithms _in Julia_ (a relatively recent language incorporating the best of R, Python and Matlab features with the efficiency of compiled languages like C or Fortran), see the companion repository [Beta Machine Learning Toolkit (BetaML)](https://github.com/sylvaticus/BetaML.jl) (and if you are looking for an introductory book on Julia, have a look on [my one](https://www.julia-book.com/)).
BetaML currently implements:
- Linear, average and kernel Perceptron (units 1 and 2)
- Feed-forward Neural Networks (unit 3)
- Clustering (k-means, k-medoids and EM algorithm), recommandation system based on EM (unit 4)
- Decision Trees / Random Forest (mentioned on unit 2)
- _many_ utility functions to help prepare the data for the analysis, sample the data or evaluate the algorithms
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**NEW 2022**: You may be interested in a new whole MOOC on [_Scientific Programming and Machine Learning with Julia_](https://sylvaticus.github.io/SPMLJ/) that covers most (but not yet all) of the topics in MITx_6.86x, with somehow a different approach, prioritising more intuition and code implementation.
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For a compact cheatsheets see the MicroMaster repository: https://github.com/sylvaticus/MITx-MicroMaster-Statistics-and-Data-Science
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[PDF all in one document](MITx_6.86x_notes.md.pdf)
By unit:
- Unit 00 - Course Overview, Homework 0, Project 0: [[pdf](Unit%2000%20-%20Course%20Overview%2C%20Homework%200%2C%20Project%200/Unit%2000%20-%20Course%20Overview%2C%20Homework%200%2C%20Project%200.md.pdf)] [[src](Unit%2000%20-%20Course%20Overview%2C%20Homework%200%2C%20Project%200/Unit%2000%20-%20Course%20Overview%2C%20Homework%200%2C%20Project%200.md)]
- Unit 01 - Linear Classifiers and Generalizations:[[pdf](Unit%2001%20-%20Linear%20Classifiers%20and%20Generalizations/Unit%2001%20-%20Linear%20Classifiers%20and%20Generalizations.md.pdf)] [[src](Unit%2001%20-%20Linear%20Classifiers%20and%20Generalizations/Unit%2001%20-%20Linear%20Classifiers%20and%20Generalizations.md)]
- Unit 02 - Nonlinear Classification, Linear regression, Collaborative Filtering: [[pdf](Unit%2002%20-%20Nonlinear%20Classification%2C%20Linear%20regression%2C%20Collaborative%20Filtering/Unit%2002%20-%20Nonlinear%20Classification%2C%20Linear%20regression%2C%20Collaborative%20Filtering.md.pdf)] [[src](Unit%2002%20-%20Nonlinear%20Classification%2C%20Linear%20regression%2C%20Collaborative%20Filtering/Unit%2002%20-%20Nonlinear%20Classification%2C%20Linear%20regression%2C%20Collaborative%20Filtering.md)]
- Unit 03 - Neural networks: [[pdf](Unit%2003%20-%20Neural%20networks/Unit%2003%20-%20Neural%20networks.md.pdf)] [[src](Unit%2003%20-%20Neural%20networks/Unit%2003%20-%20Neural%20networks.md)]
- Unit 04 - Unsupervised Learning: [[pdf](Unit%2004%20-%20Unsupervised%20Learning/Unit%2004%20-%20Unsupervised%20Learning.md.pdf)] [[src](Unit%2004%20-%20Unsupervised%20Learning/Unit%2004%20-%20Unsupervised%20Learning.md)]
- Unit 05 - Reinforcement Learning:[[pdf](Unit%2005%20-%20Reinforcement%20Learning/Unit%2005%20-%20Reinforcement%20Learning.md.pdf)] [[src](Unit%2005%20-%20Reinforcement%20Learning/Unit%2005%20-%20Reinforcement%20Learning.md)]