EE 353 – Introduction to Data Science and Machine Learning

Course Information

• Target Audience: EE Dual Degree Students. Nobody else allowed
• Lecture Days: Monday, Tuesday, Thursday (Slot 3)
• Lecture Time: 1 hour per day
• Semester Duration: July 28 – November 6, 2025
• Holidays: Aug 15, Aug 27, Sep 5, Oct 2, Oct 20, Nov 5
• Mid-Semester Break: Sep 13–21
• Last Instruction Day: Nov 6, 2025

Teaching Assistants

• 21D070030 – Jadhav Shubham Sudhakar
• 21D070048 – Prajapati Kishan Kanaiyalal
• 21D070066 – Shambhavi Shanker
• 21D070068 – Shounak Das
• 21D070086 – Varunav Singh
• 21D070064 – Saurav Raj
• 24D0515 – Gaurav Singh Bhati

Textbooks and References

• Textbook: James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in Python (2nd ed.). Springer.
• References:
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.
  • Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley.
  • Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury.

Evaluation

• Quiz 1 – 10%
• Quiz 2 – 10%
• Quiz 3 – 10%
• Mid-semester exam – 30%
• End-semester exam – 40%

There will also be 4–5 ungraded homework assignments with tutorials scheduled outside class hours.

Lecture Schedule

Week	Dates	Lectures	Topics	Code	Assignments
1	Jul 28, 29, 31	1–3	Ch 1; Ch 2; Hypothesis Testing and Sampling Distributions	Exploratory1, Exploratory2, Two-sample
2	Aug 4, 5, 7	4–6	Ch 3: Simple Regression	Simple Regression
3	Aug 11, 12, 14	7–9	Multiple Linear Regression	Multiple Regression	Multivariate Calculus Tutorial
4	Aug 18, 19, 21	10–12	Ch 4: Logistic Regression ;	Logistic Regression	Assignment 1
5	Aug 25, 26	13–14	Ch 4: Naive Bayes, LDA	LDA,QDA,NB
–	Aug 28	–	Quiz 1		Quiz1 Solutions
6	Sep 1, 2, 4	15–17	FDA1 , FDA2 ; FDA + SVM	NullLink	Assignment 2,Challenge Problem Solutions
7	Sep 8, 9, 11	18–20	SVM	NullLink
–	Sep 13–21	–	Mid-Sem	Code for Q4
8	Sep 22, 23, 25	21–23	Ch 5: LOOCV, k-Fold, C_p,AIC/BIC	NullLink, NullLink
9	Sep 29, 30	24–25	Ch 6: Best Subset, Ridge, Lasso, Model Selection	NullLink, NullLink
10	Oct 6, 7, 9	26–28	Ch 12: Clustering, PCA, Dim. Reduction ; Quiz 2	NullLink, NullLink
11	Oct 13, 14	29–30	Ch 8: Tree Methods, Bagging	NullLink, NullLink
12	Oct 21, 23	31–32	Ch 8: RF, Boosting	NullLink, NullLink
13	Oct 27, 28, 30	33–35	Ch 10: NN Basics, Forward/Backprop ; Quiz 3	NullLink, NullLink
14	Nov 3, 4, 6	36–38	Ch 10: Deep Nets	NullLink, NullLink