Schedule

Name		Name	Last commit message	Last commit date
Latest commit History 3,668 Commits
datasets		datasets
gallery		gallery
js_common		js_common
lecture_01_introduction		lecture_01_introduction
lecture_02_python		lecture_02_python
lecture_03_numpy		lecture_03_numpy
lecture_04_pandas		lecture_04_pandas
lecture_05_matplotlib		lecture_05_matplotlib
lecture_06_optimization		lecture_06_optimization
lecture_07_naive_bayes		lecture_07_naive_bayes
lecture_08_statistics		lecture_08_statistics
lecture_09_linear_regression		lecture_09_linear_regression
lecture_10_support_vector_machines		lecture_10_support_vector_machines
lecture_11_decision_trees_and_random_forest		lecture_11_decision_trees_and_random_forest
lecture_12_principal_component_analysis		lecture_12_principal_component_analysis
lecture_13_k_means_clustering		lecture_13_k_means_clustering
lecture_14_gaussian_mixture_models		lecture_14_gaussian_mixture_models
lecture_15_kernel_density_estimation		lecture_15_kernel_density_estimation
lecture_16_manifold_learning		lecture_16_manifold_learning
pytutor		pytutor
README.md		README.md
bureaucracy.md		bureaucracy.md
caption.png		caption.png
end.gif		end.gif
gallery.ipynb		gallery.ipynb
gallery.md		gallery.md
game_over.png		game_over.png
geotag.png		geotag.png
know_how.md		know_how.md
lecture_01_introduction.ipynb		lecture_01_introduction.ipynb
lecture_02_python.ipynb		lecture_02_python.ipynb
lecture_03_numpy_arrays.ipynb		lecture_03_numpy_arrays.ipynb
lecture_04_pandas.ipynb		lecture_04_pandas.ipynb
lecture_05_matplotlib.ipynb		lecture_05_matplotlib.ipynb
lecture_06_optimization.ipynb		lecture_06_optimization.ipynb
lecture_07_naive_bayes.ipynb		lecture_07_naive_bayes.ipynb
lecture_08_statistics.ipynb		lecture_08_statistics.ipynb
lecture_09_linear_regression.ipynb		lecture_09_linear_regression.ipynb
lecture_10_support_vector_machines.ipynb		lecture_10_support_vector_machines.ipynb
lecture_11_decision_trees_and_random_forest.ipynb		lecture_11_decision_trees_and_random_forest.ipynb
lecture_12_principal_component_analysis.ipynb		lecture_12_principal_component_analysis.ipynb
lecture_13_k_means_clustering.ipynb		lecture_13_k_means_clustering.ipynb
lecture_14_gaussian_mixture_models.ipynb		lecture_14_gaussian_mixture_models.ipynb
lecture_15_kernel_density_estimation.ipynb		lecture_15_kernel_density_estimation.ipynb
lecture_16_manifold_learning.ipynb		lecture_16_manifold_learning.ipynb
lecture_17_neural_networks.pdf		lecture_17_neural_networks.pdf
methodological_notes.ipynb		methodological_notes.ipynb
midterm_1.md		midterm_1.md
midterm_2.md		midterm_2.md
next_iteration.md		next_iteration.md
python_refresher.ipynb		python_refresher.ipynb
questions.txt		questions.txt
test_basic_functions.ipynb		test_basic_functions.ipynb
test_ground.ipynb		test_ground.ipynb
workbook_01_introduction.ipynb		workbook_01_introduction.ipynb
workbook_02_python.ipynb		workbook_02_python.ipynb
workbook_03_numpy_arrays.ipynb		workbook_03_numpy_arrays.ipynb
workbook_04_pandas.ipynb		workbook_04_pandas.ipynb
workbook_05_matplotlib.ipynb		workbook_05_matplotlib.ipynb
workbook_06_optimization.ipynb		workbook_06_optimization.ipynb
workbook_07_naive_bayes.ipynb		workbook_07_naive_bayes.ipynb
workbook_08_statistics.ipynb		workbook_08_statistics.ipynb
workbook_09_linear_regression.ipynb		workbook_09_linear_regression.ipynb
workbook_10_support_vector_machines.ipynb		workbook_10_support_vector_machines.ipynb
workbook_11_decision_trees_and_random_forest.ipynb		workbook_11_decision_trees_and_random_forest.ipynb
workbook_12_principal_component_analysis.ipynb		workbook_12_principal_component_analysis.ipynb
workbook_13_k_means_clustering.ipynb		workbook_13_k_means_clustering.ipynb
workbook_14_gaussian_mixture_models.ipynb		workbook_14_gaussian_mixture_models.ipynb
workbook_15_kernel_density_estimation.ipynb		workbook_15_kernel_density_estimation.ipynb
workbook_16_manifold_learning.ipynb		workbook_16_manifold_learning.ipynb

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