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https://github.com/mayer79/ml_lecture
intro to ML
https://github.com/mayer79/ml_lecture
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intro to ML
- Host: GitHub
- URL: https://github.com/mayer79/ml_lecture
- Owner: mayer79
- Created: 2020-08-14T08:21:47.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-03T08:21:17.000Z (4 months ago)
- Last Synced: 2024-12-11T15:49:33.060Z (14 days ago)
- Language: Jupyter Notebook
- Size: 111 MB
- Stars: 29
- Watchers: 6
- Forks: 18
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Introduction to Machine Learning
#### Michael Mayer
## Overview
Welcome to this training course on machine learning (ML).
It is both available in R and Python.
ML can be viewed as a collection of statistical algorithms used to
1. predict values (supervised ML) or to
2. investigate data structure (unsupervised ML).
Our focus is on *supervised ML*. Depending on if we predict numbers or classes, we talk about *regression* or *classification*.
## Copyright
This lecture is being distributed under the [creative commons license](https://creativecommons.org/licenses/by/2.0/).
## How to cite?
Michael Mayer. Introduction to Machine Learning (2024). Web: https://github.com/mayer79/ml_lecture/
## Organization
The lecture is split into four chapters, each of which is accompanied with an R/Python notebook and exercise solutions. You will find them in the corresponding subfolders.
1. Basics and Linear Models
- Basics
- Linear regression
- Generalized Linear Model
2. Model Selection and Validation
3. Trees
- Decision trees
- Random forests
- Gradient boosting
4. Neural Nets
Each chapter will take us about two hours. You will do the exercises on your own.
## Lecture notes
The lecture notes are available both as Jupyter notebooks (Python) and HTML (R).
### Python
1. [Basics and Linear Models](https://github.com/mayer79/ml_lecture/blob/main/py/1_Basics_and_Linear_Models.ipynb)
2. [Model Selection and Validation](https://github.com/mayer79/ml_lecture/blob/main/py/2_Model_Selection_and_Validation.ipynb)
3. [Trees](https://github.com/mayer79/ml_lecture/blob/main/py/3_Trees.ipynb)
4. [Neural Nets](https://github.com/mayer79/ml_lecture/blob/main/py/4_Neural_Nets.ipynb)
### R
1. [Basics and Linear Models](https://mayer79.github.io/ml_lecture/1_Basics_and_Linear_Models.html)
2. [Model Selection and Validation](https://mayer79.github.io/ml_lecture/2_Model_Selection_and_Validation.html)
3. [Trees](https://mayer79.github.io/ml_lecture/3_Trees.html)
4. [Neural Nets](https://mayer79.github.io/ml_lecture/4_Neural_Nets.html)
## Prerequisites
To follow the lecture, you should be familiar with
- Descriptive statistics
- Linear regression
- R or Python
To get the material, clone this repository via
```
git clone https://github.com/mayer79/ml_lecture.git
```
### Python setup
Python 3.11 and the packages specified [here](py/requirements.txt).
### R setup
R version >= 4.1 and these packages: tidyverse, FNN, withr, rpart.plot, ranger, xgboost, keras, hstats, MetricsWeighted, insuranceData, lightgbm
For the last chapter, we will use Python with TensorFlow >= 2.15. You can install it by running the R command `keras::install_keras(version = "release-cpu")`. If the following code works, you are all set. (Some red start-up messages/warnings are okay.)
```
library(tensorflow)
tf$constant("Hello Tensorflow!")
```
## Further Reading
- James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). *An Introduction to Statistical Learning - with Applications in R*. New York: Springer.
- James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. (2023). *An Introduction to Statistical Learning - with Applications in Python*. New York: Springer.
- Hastie, T., Tibshirani, R., Friedman, J. (2001). *The Elements of Statistical Learning: Data Mining, Inference, and Prediction*. New York: Springer.
- Wickham, H., Grolemund, G. (2017). *R for Data Science: Import, Tidy, Transform, Visualize, and Model Data*. O'Reilly Media.
- VanderPlas, J. (2016). *Python data science handbook : essential tools for working with data*. O'Reilly Media.
- Chollet, F. (2017). *Deep Learning with Python*. Manning Publications Co.
- Chollet, F., Allaire, J. J. (2018). *Deep Learning with R*. Manning Publications Co.