Course Schedule
| Session | Day | Date | Topic | Details | References |
|---|---|---|---|---|---|
| 1 | Sat | 2 Esfand | Mathematical Foundations | Course overview and mathematical review | — |
| 2 | Mon | 4 Esfand | — | — | — |
| 3 | Sat | 9 Esfand | — | — | — |
| 4 | Mon | 11 Esfand | — | — | — |
| 5 | Sat | 16 Esfand | Introduction to Supervised Learning | Input/output data, data types, numerical encoding, model definition, model complexity, parametric vs. nonparametric models, loss functions, bias–variance tradeoff | ESL 2.2–2.3 |
| 6 | Mon | 18 Esfand | Linear Models | Simple linear regression, coefficient estimation, inference, model assessment | ISL 3.1 |
| 7 | Sat | 23 Esfand | Linear Models | Multiple linear regression, coefficient estimation | ISL 3.2 |
| 8 | Mon | 25 Esfand | Linear Models | Nonlinear feature expansion, model limitations | ESL 5.1; ISL 3.3, 7.1–7.3 |
| 9 | Sat | 15 Farvardin | Linear Classification | Logistic regression | ISL 4.1–4.3 |
| 10 | Mon | 17 Farvardin | Generative Models | Linear Discriminant Analysis (LDA) | ISL 4.4–4.5 |
| 11 | Sat | 22 Farvardin | Model Evaluation & Selection | Bias–variance tradeoff, model complexity | ESL 7.2–7.5 |
| 12 | Mon | 24 Farvardin | Model Selection | Subset selection methods | ESL 3.3 |
| 13 | Sat | 29 Farvardin | Regularization | Ridge regression (L2) | ESL 3.4 |
| 14 | Mon | 31 Farvardin | Regularization | Lasso regression (L1) | ISL 3.4 |
| 15 | Sat | 5 Ordibehesht | Complexity Estimation | Cross-validation (K-fold), Bootstrap | ESL 7.10–7.11 |
| 16 | Mon | 7 Ordibehesht | Tree-Based Models | Decision trees and regression trees | ISL 8.1; ESL 9.2 |
| 17 | Sat | 12 Ordibehesht | Tree-Based Models | Pruning, strengths and weaknesses | ISL 8.1; ESL 9.2 |
| 18 | Mon | 14 Ordibehesht | Support Vector Machines | Maximum margin classifier, SVC, SVM | ESL 12.1–12.3; ISL 9.1–9.3 |
| 19 | Sat | 19 Ordibehesht | Support Vector Machines | Kernel trick, multiclass SVM | ESL 12.3; ISL 9.4–9.5 |
| 20 | Mon | 21 Ordibehesht | Neural Networks | Perceptron and neural network foundations | ISL 10.1–10.2 |
| 21 | Sat | 26 Ordibehesht | Neural Networks | Multilayer perceptron (MLP) and architectures | ISL 10.3–10.5 |
| 22 | Mon | 28 Ordibehesht | Optimization | Stochastic Gradient Descent (SGD) | ESL 11.4 |
| 23 | Sat | 2 Khordad | Ensemble Methods | Bagging, Random Forest | ESL 15.1–15.4 |
| 24 | Mon | 4 Khordad | Ensemble Methods | AdaBoost | ESL 10.1–10.4 |
| 25 | Sat | 9 Khordad | Ensemble Methods | Gradient Boosting | ESL 10.10 |
| 26 | Mon | 11 Khordad | Unsupervised Learning | K-means, Gaussian Mixture Models (GMM), Soft K-means | ESL 14.3 |
| 27 | Sat | 16 Khordad | Dimensionality Reduction | Principal Component Analysis (PCA) | ESL 14.4 |
| 28 | Mon | 18 Khordad | Dimensionality Reduction | Independent Component Analysis (ICA) | ESL 14.5 |
| 29 | Sat | 23 Khordad | Nonlinear Manifold Learning | Isomap | ESL 14.9 |
| 30 | Mon | 25 Khordad | Reserved | — | — |
| 31 | Sat | 30 Khordad | Reserved | — | — |
| 32 | Mon | 1 Tir | Reserved | — | — |
Final Exam
15 Tir – 14:00