| 1 |
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Introduction to Machine Learning |
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| 2 |
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Introduction to python and jupyter notebook |
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| 3 |
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Naïve bayes |
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| 4 |
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Naïve bayes-implementation and more examples |
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| 5 |
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Decision Tree |
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| 6 |
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Descision Tree- model evaluation |
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| 7 |
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Confusion matrix-Overfitting-K nearest Neighbor |
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| 8 |
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Linear and logistic regression |
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| 9 |
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Neural network and Deep learning |
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| 10 |
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MID-TERM EXAM |
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| 11 |
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Reinforcement learning |
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| 12 |
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Clustering |
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| 13 |
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Project presentations |
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| 14 |
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Project presentations |
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| 15 |
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Project presentations |
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| 16 |
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FINAL EXAM |
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| 17 |
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FINAL EXAM |
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