Machine Learning (CSE)
Machine Learning (CSE)
Introduction : [Lecture_Notes] [PPT] [Video_Lecture]
Introduction-Syllabus : [Lecture_Notes] [PPT] [Video_Lecture]
Spam Email Detection: [Lecture_Notes] [PPT] [Video_Lecture]
Tasks and their Classification: [Lecture_Notes] [PPT] [Video_Lecture]
Models, Geometric Models: [Lecture_Notes] [PPT] [Video_Lecture]
Kmeans Clustering, Probabilistic Models : [Lecture_Notes] [PPT] [Video_Lecture]
Logical Models, Grouping & Grading Models : [Lecture_Notes] [PPT] [Video_Lecture]
Features, Machine Learning Process : [Lecture_Notes] [PPT] [Video_Lecture]
1. The curse of dimensionality [Lecture_Notes ] [PPT] [Video_Lecture]
2. Training, Test and Validation sets, The confusion matrix, The accuracy metrics: Accuracy, sensitivity, specificity, precision, recall, F1 measure [Lecture Notes] [PPT] [Video_Lecture]
3. Prior Probability, Conditional Probability, Naïve Bayesian Classifier [Lecture_Notes] [PPT] [Video_Lecture]
4. Naïve Bayesian Classifier Example [Lecture_Notes] [PPT] [Video_Lecture]
5. Some Basic Statistics: Variance, Covariance, Bias-Variance Tradeoff , Overfitting , Underfitting, ROC Curve, Unbalanced Datasets [Lecture_Notes] [PPT] [Video_Lecture]
Tree Models : Decision Trees [Lecture_Notes][PPT] [Video_Lecture]
Linear Models
Univariate Linear Regression [Lecture_Notes] [Method_of_Least_Squares] [PPT 1] [PPT 2] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3]
Logistic Regression [Lecture_Notes][PPT] [Video_Lecture]
Support Vector Machines
Part 1 [Lecture_Notes][PPT] [Video_Lecture] [Extra_Resource_Link]
Part 2 [Lecture_Notes][PPT] [Video_Lecture]
Distance Based Models
Distance Functions [Lecture_Notes] [PPT][Video_Lecture]
Neighbours and Exemplars [Lecture_Notes][PPT] [Video_Lecture]
Nearest Neighbour Classification [Lecture_Notes][PPT] [KNN_Example] [Video_Lecture]
Distance Based Clustering
K-Means Algorithms [Lecture_Notes][PPT] [K-Means_Example] [Video_Lecture]
K-Medoids Algorithm and Silhouettes [Lecture_Notes][PPT] [Video_Lecture]
Hierarchical Clustering [Lecture_Notes][PPT] [Video_Lecture]
Features
Part 1 [Lecture_Notes] [PPT] [Video_Lecture]
Part 2 [Lecture_Notes] [PPT] [Video_Lecture1] [Video_Lecture2]
Part 3 [Lecture Notes] [PPT] [Video_Lecture]
2. Ensemble Models
Bagging, Random Forests, Boosting: AdaBoost [Lecture_Notes] [PPT] [Video_Lecture1] [Video_Lecture2]
Gradient Boosting [Lecture_Notes] [PPT1] [PPT2] [Video_Lecture1] [Video_Lecture2] [Class_Video_Lecture]
XGBoost [Lecture_Notes] [PPT] [Video_Lecture] [Class_Video_Lecture]
PCA [Lecture_Notes] [PPT] [Math_Behind_PCA] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3]
LDA [Lecture_Notes] [PPT] [LDA_Numerical_Example][Video_Lecture1] [Video_Lecture2] [Video_Lecture3] [Video_Lecture4]
Cross Validation and Grid Search [Lecture_Notes] [PPT] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3]
Regularization [Lecture_Notes] [PPT] [L1_and_L2_Regularization] [Data_Augmentation_Techniques_for_Images] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3] [Video_Lecture4]
Neurons, NNs, Linear Discriminants [Lecture_Notes] [PPT] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3] [Video_Lecture4] [Video_Lecture5]
Multilayer Perceptrons [Lecture_Notes] [PPT1][PPT2] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3] [Video_Lecture4] [Video_Lecture5]
Reinforcement Learning [Lecture_Notes] [Example][PPT] [Video_Lecture1] [Video_Lecture2] [Video_Lecture3] [Video_Lecture4]