Machine Learning (ML)

ML CS303, Odd Sem 2025-26, DTU
A2 Batch, 3rd Year
Mon 2-3 PM in AB4 015, Thu 2-3 PM and Fri 12-1 PM in AB4 315

Instructor: Garima Chhikara
Email: garimachhikara@dtu.ac.in
Links:


Class No. Date Topic Class Notes Supplementary Material
1 4 Aug 2025 Introduction Aug 4
2 7 Aug 2025 Linear Regression Aug 7 Notes Video
3 8 Aug 2025 Linear Regression Example Aug 8
4 11 Aug 2025 Logistic Regression Aug 11 Notes Video
5 18 Aug 2025 Logistic Regression Aug 18
6 21 Aug 2025 KNN, Naive Bayes Aug 21 Notes Video Video
7 22 Aug 2025 Decision Trees Aug 22 Notes Video
8 26 Aug 2025 Decision Trees Aug 26
9 1 Sep 2025 SVM Sep 1 Notes Video
10 8 Sep 2025 SVM Sep 8
11 11 Sep 2025 Classification Metrics Sep 11 Video Video Video Video
12 15 Sep 2025 Classification Metrics Sep 15
13 10 Oct 2025 Neural Networks Oct 10 Video (Lec 1 to 39)
14 13 Oct 2025 Neural Networks Numerical Oct 13
15 16 Oct 2025 Neural Networks Numerical Oct 16
30 Oct 2025 Mid Term Sheets Shown
16 3 Nov 2025 Batching, Clustering: K-Means Nov 3 Video
17 5 Nov 2025 PCA Nov 5 Video (Lec 46 to 49)
18 6 Nov 2025 t-SNE, Fairness, Explainable AI Nov 6 Video Video Video
19 7 Nov 2025 Reinforcement Learning Nov 7 Video (Lec 1 to 6)

Unit Topic Resources
1 Random Variable Link
Expectation and Variance of Discrete Random Variable Link
Expectation and Variance of Continuous Random Variable Link
Conditional Probability Link
Baye's Theorem Link
Probability Distribution Link Link
PDF, PMF, CDF Link
2 Handling Missing Data Link
Outlier Detection Link
Categorical Data Encoding Link
Feature Scaling Link
Feature Selection Methods Link Link
Exploratory Data Analysis Link
Population and Sample Link Link
Sampling Techniques Link

Grading Policy

Acknowledgment

Notes and Videos shared in the supplementary material are adapted from ML course of Prof. Parag Singla (IITD) and You Tube channel of CampusX respectively.