SIXTH SEM JAN 2026

After completion of the course, the students will be able to:

CO1: Understand and explain key machine learning concepts,

including learning problems, system design, and inductive bias.

CO2: Apply model validation techniques like K-Fold Cross-Validation

and Bootstrapping to assess model performance.

CO3: Implement and evaluate regression and classification algorithms,

with a focus on handling overfitting and underfitting.

CO4: Build and optimize decision trees, apply ensemble methods, and

use Bayesian approaches for classification.

CO5: Use clustering techniques, perform error analysis, and apply

dimensionality reduction and feature selection to improve

models.

CO6: Design and implement neural networks and understand the

basics of deep learning.