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Tutorials for the AIMS students 15/16

Tutorials

Tutorials for the AIMS students 15/16

Git Tutorials (from http://gitimmersion.com/). Work offline, just double click on the index file from the Git_folder.

 Table of Contents
1: Setup
2: More Setup
3: Create a Project
4: Checking Status
5: Making Changes
6: Staging Changes
7: Staging and Committing
8: Committing Changes
9: Changes, not Files
10: History
11: Aliases
12: Getting Old Versions
13: Tagging versions
14: Undoing Local Changes (before staging)
15: Undoing Staged Changes (before committing)
16: Undoing Committed Changes
17: Removing Commits from a Branch
18: Remove the oops tag
19: Amending Commits
20: Moving Files
21: More Structure
22: Git Internals: The .git directory
23: Git Internals: Working directly with Git Objects
24: Creating a Branch
25: Navigating Branches
26: Changes in Master
27: Viewing Diverging Branches
28: Merging
29: Creating a Conflict
30: Resolving Conflicts
31: Rebasing VS Merging
32: Resetting the Greet Branch
33: Resetting the Master Branch
34: Rebasing
35: Merging Back to Master
36: Multiple Repositories
37: Cloning Repositories
38: Review the Cloned Repository
39: What is Origin?
40: Remote Branches
41: Change the Original Repository
42: Fetching Changes
43: Merging Pulled Changes
44: Pulling Changes
45: Adding a Tracking Branch
46: Bare Repositories
47: Adding a Remote Repository
48: Pushing a Change
49: Pulling Shared Changes
50: Hosting your Git Repositories
51: Sharing Repos
52: Advanced / Future Topics
53: Thank you

Machine Learning Tutorial - Thanks to Jacob VanderPlas (github adress http://astroML.github.com/sklearn_tutorial/)

1. Tutorial Setup and Installation
    1.1. Python Prerequisites
    1.2. Tutorial Files
    1.3. Download the datasets
2. Machine Learning 101: General Concepts
    2.1. Features and feature extraction
    2.2. Supervised Learning, Unsupervised Learning, and scikit-learn syntax
    2.3. Supervised Learning: model.fit(X, y)
    2.4. Unsupervised Learning: model.fit(X)
    2.5. Linearly separable data
    2.6. Hyperparameters, training set, test set and overfitting
    2.7. Key takeaway points
3. Machine Learning 102: Practical Advice
    3.1. Bias, Variance, Over-fitting, and Under-fitting
    3.2. Cross-Validation and Testing
    3.3. Learning Curves
    3.4. Summary
4. Classification: Learning Labels of Astronomical Sources
    4.1. Motivation: Why is this Important?
    4.2. Star-Quasar Classification: Naive Bayes
5. Regression: Photometric Redshifts of Galaxies
    5.1. Motivation: Dark Energy, Dark Matter, and the Fate of the Universe
    5.2. A Simple Method: Decision Tree Regression
6. Dimensionality Reduction of Astronomical Spectra
    6.1. SDSS Spectral Data
    6.2. Principal Component Analysis
    6.3. References
7. Exercises: Taking it a step further
    7.1. Exercise 1: Photometric Classification with GMM
    7.2. Exercise 2: Photometric redshifts with Decision Trees
    7.3. Exercise 3: Dimensionality Reduction of Spectra
8. Code examples

© scikit-learn developers.