EE 353 – Introduction to Data Science and Machine Learning

Course Information

Instructors:

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 R (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

WeekDatesLecturesTopicsSlides
1Jul 28, 29, 311–3Ch 1; Ch 2; Hypothesis Testing and Sampling Distributions
2Aug 4, 5, 74–6Ch 3: Simple & Multiple Linear Regression
3Aug 11, 12, 147–9Ch 3: Interactions, Qual Vars, Diagnostics
4Aug 18, 19, 2110–12Ch 4: Logistic Regression; Quiz 1
5Aug 25, 2613–14Ch 4: Naive Bayes, ROC, LDA
6Sep 1, 2, 415–17Ch 9: Kernel SVM; Tuning
7Sep 8, 9, 1118–20Ch 5: LOOCV, k-Fold, Bootstrap
–Sep 13–21–Mid-Sem Break
8Sep 22, 23, 2521–23Ch 6: Best Subset, Ridge, Lasso
9Sep 29, 3024–25Ch 6: Model Selection
10Oct 6, 7, 926–28Ch 12: Clustering, PCA, Dim. Reduction; Quiz 2
11Oct 13, 1429–30Ch 8: Tree Methods, Bagging
12Oct 21, 2331–32Ch 8: RF, Boosting
13Oct 27, 28, 3033–35Ch 10: NN Basics, Forward/Backprop; Quiz 3
14Nov 3, 4, 636–38Ch 10: Deep Nets