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Week - 1 |
Overview In Machine Learning Basics: Evaluation of basic algorithms |
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Week - 2 |
Train and test data sets, Overtraining |
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Week - 3 |
Accuracy and confusion matrix and other metrics |
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Week - 4 |
ROC curves |
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Week - 5 |
Conditional Probabilities: Conditional expectations and loss function |
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Week - 6 |
Discriminative approaches (QDA, LDA) |
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Week - 7 |
Naive Bayes algorithms:Simple linear regression |
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Week - 8 |
Smoothing and matrix algebra: Distance, Cross-validation |
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Week - 9 |
K-Nearest neighbour |
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Week - 10 |
Support vector machine |
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Week - 11 |
Classification; Classification with two classes |
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Week - 12 |
Random forest |
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Week - 13 |
Principal component analysis |
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Week - 14 |
K-means algorithm. |