BEC-351: Fundamentals of AI/ML

Autumn Semester 2025-26

मूलं हि शस्यं वर्धते नितान्तं

मूलं हि विद्या वर्धते नितान्तम्।

मूलं हि सर्वस्य भवन्ति मूलं

मूलं न सन्देहकृतं हि लोके॥

Just as the root is essential for the growth of a plant,

the fundamentals of knowledge are crucial for its development.

The root is the basis of everything;

there is no doubt about this in the world.

Course Information

15-July-2025: Course Announcements will be posted here regularly. Email notifications will only be sent if information is urgent.

Theoretical underpinnings of learning from data
Bigger picture of how energy functions and loss functions guide model training and evaluation
Various loss functions
First-order optimization methods, including gradient descent (GD) and stochastic gradient descent (SGD)
Basics of constrained optimization and its relevance in training machine learning models
Hyperparameter tuning strategies to optimize model performance
Validation techniques to assess model generalization
Evaluation metrics to measure and compare the effectiveness of different models
Bayesian inference in machine learning

Python is the default programming languages for the course. You should use it for programming your assignments unless otherwise explicitly allowed.
Submit via Moodle or GitHub—- as specified in each assignment.
Honor Code: Any cases of copying will be awarded a zero on the assignment. More severe penalties may follow.
Late submissions will incur penalties, as announced with assignment.

Recommended Texts:

Probabilistic Machine Learning: An Introduction, Kevin Murphy. MIT Press, 2022/2023.

Supplementary Resources:

Movies:

Continuous Assessment (CWS): 30%
- - Announced & Surprise Quizzes
  - Assignments
Mid-Term Exam (MTE): 30%
End-Term Exam (ETE): 40%

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