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	Data types, model definition, complexity, bias–variance	ESL 2.2–2.3
6	Mon	18 Esfand	Linear Models	Simple linear regression	ISL 3.1
7	Sat	23 Esfand	Linear Models	Multiple linear regression	ISL 3.2
8	Mon	25 Esfand	Linear Models	Nonlinear feature expansion	ESL 5.1; ISL 3.3
9	Sat	15 Farvardin	Linear Classification	Logistic regression	ISL 4.1–4.3
10	Mon	17 Farvardin	Generative Models	Linear Discriminant Analysis	ISL 4.4–4.5
11	Sat	22 Farvardin	Model Evaluation	Bias–variance tradeoff	ESL 7.2–7.5
12	Mon	24 Farvardin	Model Selection	Subset selection	ESL 3.3
13	Sat	29 Farvardin	Regularization	Ridge (L2)	ESL 3.4
14	Mon	31 Farvardin	Regularization	Lasso (L1)	ISL 3.4
15	Sat	5 Ordibehesht	Complexity Estimation	Cross-validation, Bootstrap	ESL 7.10–7.11
16	Mon	7 Ordibehesht	Tree-Based Models	Decision & regression trees	ISL 8.1; ESL 9.2
17	Sat	12 Ordibehesht	Tree-Based Models	Pruning	ISL 8.1; ESL 9.2
18	Mon	14 Ordibehesht	Support Vector Machines	MMC, SVC, SVM	ESL 12.1–12.3
19	Sat	19 Ordibehesht	Support Vector Machines	Kernel trick	ISL 9.4–9.5
20	Mon	21 Ordibehesht	Neural Networks	Perceptron	ISL 10.1–10.2
21	Sat	26 Ordibehesht	Neural Networks	MLP	ISL 10.3–10.5
22	Mon	28 Ordibehesht	Optimization	Stochastic Gradient Descent	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, GMM	ESL 14.3
27	Sat	16 Khordad	Dimensionality Reduction	PCA	ESL 14.4
28	Mon	18 Khordad	Dimensionality Reduction	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