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Expand Up @@ -74,10 +74,13 @@ Table of contents
The course material is posted here and you can use either [Google
Colab](http://colab.research.google.com/) or [Mybinder](http://mybinder.org/) to
work with these Jupyter notebooks.
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sraeisi/MachineLearning_Physics/master)

[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sraeisi/MachineLearning_Physics/)

| Topic | Contents of the Lectures | Notebook(s) |
|---------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
| Basics of machine learning | Introduction and notation <br> Regression, logistic regression and classification <br> overview and practical tips <br> Why? ML beyond simple examples <br> Overview of some of the most common techniques | |
| Basics of machine learning | Introduction and notation <br> Regression, logistic regression and classification <br> overview and practical tips <br> Why? ML beyond simple examples <br> Overview of some of the most common techniques | Lecture 1 <br> [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sraeisi/MachineLearning_Physics/master?filepath=Lec_1%2FMLP_lec_1_Introductory_notes_A.ipynb) <br> [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sraeisi/MachineLearning_Physics/blob/master/Lec_1/MLP_lec_1_Introductory_notes_A.ipynb) |
| Data Preparation | Collection and generation <br> Standardization <br>Clean-up: nan and outliers <br> Data reduction: PCA, variance threshold … | |
| Concepts from Statistical learning | Variance and bias <br> Learning curves <br> Model selection and validation curve <br> Practical methods for dealing with overfitting <br> Bayesian inference | |
| A few tools before we get down to it… | Cost functions <br> Optimization algorithms | |
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