| Lec 1 |
Introduction to Machine Learning |
Overview of machine learning paradigms, key applications, and fundamental challenges. |
📥 PDF |
| Lec 2 |
Decision Trees |
Principles of decision trees, feature splitting, tree construction, Decision Tree Decision Boundaries, practical challenges |
📥 PDF |
| Lec 3 |
Estimation Strategy and Evaluation Metrics |
Estimation Strategy (Holdeout method, K-Fold Cross Validation, LOOV), and Evaluation Metrics (Precision, Recall, F1, Sensitivity vs Specificity, The Area Under the Curve, Precision-Recall curve). |
📥 PDF |
| Lec 4 |
Feature Engineering |
Feature Preprocessing, Feature Selection, Feature Extraction (Univariate feature selection, Multivariate feature selection) |
📥 PDF |
| Lec 5 |
Support Vector Machines (SVM) |
Intuitions, SVM Optimization, Soft Margin SVM (C Hyper-Parameters), Gamma Hyper-Parameters, The Kernel Trick, Multiclass classification (One-against-all, One-vs-one), Feature scaling
|
📥 PDF |
| Lec 6 |
KNN and NB |
Fundamentals of K-Nearest Neighbors and Naive Bayes classifiers with practical use cases. |
📥 PDF |
| Lec 7 |
Practical Concerns for Machine Learning 1 |
Generalization, Overfitting, underfitting, and bias-variance tradeoff |
📥 PDF |
| Lec 8 |
Practical Concerns for Machine Learning 2 |
Ensemble Methods, Imbalanced Dataset handling |
📥 PDF |
| Lec 9 |
Introduction to Deep Learning |
Training , Optimization, and Regularization |
📥 PDF |
| Lec 10 |
Well Known DL Architecture (till 2016) |
CNN, RNN, LSTM |
📥 PDF |
| Lec 11 |
Transformers |
Transformers, attention mechanism, encoder part, decoder part, masked attention, cross attention |
📥 PDF |
