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10_Hypothesis_Testing.ipynb
10_Hypothesis_Testing.ipynb
1_Sets_and_Counting_mGalarnyk.ipynb
1_Sets_and_Counting_mGalarnyk.ipynb
2_Probability_spaces.ipynb
2_Probability_spaces.ipynb
3_Multiple_events_ conditioning_and_independence.ipynb
3_Multiple_events_ conditioning_and_independence.ipynb
4_RandomVariable_Expectation_Variance.ipynb
4_RandomVariable_Expectation_Variance.ipynb
5_Generative_Models_I_Class_Generators.ipynb
5_Generative_Models_I_Class_Generators.ipynb
6_Generative_Models_II.ipynb
6_Generative_Models_II.ipynb
6_Generative_Models_number_9-FINAL.ipynb
6_Generative_Models_number_9-FINAL.ipynb
7_Clustering.ipynb
7_Clustering.ipynb
8_Matrix_Factorization.ipynb
8_Matrix_Factorization.ipynb
9_Sampling.ipynb
9_Sampling.ipynb
IRIS data set.ipynb
IRIS data set.ipynb
IRIS data set.pdf
IRIS data set.pdf
K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset.ipynb
K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset.ipynb
Other_worksheet.pdf
Other_worksheet.pdf
README.md
README.md
iris.txt
iris.txt
loader.py
loader.py
t10k-images-idx3-ubyte
t10k-images-idx3-ubyte
t10k-images-idx3-ubyte.npy
t10k-images-idx3-ubyte.npy
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t10k-labels-idx1-ubyte
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t10k-labels-idx1-ubyte.npy
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train-images-idx3-ubyte.npy
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DSE210_Probability_Statistics_Python

Looks best on google chrome. Probability and Statistics Using Python: Data Science Masters Course (DSE 210). Highly similar to UCSD's "CSE 250B. Principles of Artificial Intelligence: Learning Algorithms" course. Most of the early portions of the class are worksheet based, but the later portions are mostly in ipython notebook (numpy, sklearn, pandas).

IPython Notebooks for Assignments (From Newest to Oldest)

  • [Hypothesis testing](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/10_Hypothesis_Testing.ipynb) (1, 2, 6, 7, 8, 9, 10)
  • [Sampling](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/9_Sampling.ipynb) (1, 3, 5, 8, 9, 10, 11)
  • [Matrix factorization](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/8_Matrix_Factorization.ipynb) # 1,2,3,4 (PCA Projection, python),5 (PCA Projection, python)
  • [Clustering](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/7_Clustering.ipynb)
  • [Generative models 2](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/6_Generative_Models_number_9-FINAL.ipynb) Gaussian Classifier
  • [Generative models 1](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/5_Generative_Models_I_Class_Generators.ipynb) (object oriented, next up is pandas and sql)
  • [Random variable, expectation, and variance](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/4_RandomVariable_Expectation_Variance.ipynb) (1,2,3,6,7a,7c,8,12)
  • [Multiple events, conditioning, and independence](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/3_Multiple_events_%20conditioning_and_independence.ipynb) (1,2,3,5,6,10,15a,16)
  • [Probability spaces](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/2_Probability_spaces.ipynb) (1a,1b,1e,2,3,4a,5,6,7,14,16)
  • [Sets and counting](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/1_Sets_and_Counting_mGalarnyk.ipynb) (1,2,3,4,5,6)

    • Worksheets (From Newest to Oldest)

  • [Hypothesis testing](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet10Hypothesis_testing.pdf) (1, 2, 6, 7, 8, 9, 10)
  • [Sampling](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet9Sampling.pdf) (1, 3, 5, 8, 9, 10, 11)
  • [Matrix factorization](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet8Matrix_factorization.pdf) (1,2,3,4,5)
  • [Clustering](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet7Clustering.pdf) (all)
  • [Generative models 2](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet6GenerativeModels2.pdf) (#9 python based Gaussian Classifier)
  • [Generative models 1](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet5Generative_models_1.pdf) (all)
  • [Random variable, expectation, and variance](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet4Random_variable_expectation_and_variance.pdf) (1,2,3,6,7a,7c,8,12)
  • [Multiple events, conditioning, and independence](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet3_Multiple_events_%20conditioning_and_independence.pdf) (1,2,3,5,6,10,15a,16)
  • [Probability spaces](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet2_Probability_spaces.pdf) (1a,1b,1e,2,3,4a,5,6,7,14,16)
  • [Sets and counting](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet1_Sets_and_counting.pdf) (1,2,3,4,5,6)

    • Other (Iris Dataset plus other scratch worksheet)

  • [K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/K-Means%2C%20PCA%2C%20and%20Dendrogram%20on%20the%20Animals%20with%20Attributes%20Dataset.ipynb)

  • [Iris Dataset](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/IRIS%20data%20set.ipynb)
  • [Scratch stats problems](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/Other_worksheet.pdf)

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    Probability and Statistics Using Python Data Science Masters Course at UCSD (DSE 210)

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