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