Advanced Machine Learning
Advanced ML Labs
Lab Syllabus
| Sr. No. | Title of the experiment | CO |
|---|---|---|
| 1 | Build generalized Linear Model. | CO1 |
| 2 | Develop a regression model for count data using Poisson Regression, a member of the exponential family. | CO1 |
| 3 | Implement directed Graphical model | CO2 |
| 4 | Implement Factor Analysis for any real world problem. | CO3 |
| 5 | Implement EM algorithm for given problem. | CO3 |
| 6 | Develop a HMM for real world problem (example weather forecasting) | CO4 |
| 7 | Implement the Viterbi algorithm to find the most likely sequence of hidden states (POS tags) given a sequence of observations (words). | CO4 |
| 8 | Implement undirected graphical model. | CO5 |
| 9 | Implement a linear-chain CRF for a sequence labeling task, such as named-entity recognition (NER). | CO5 |
| 10 | Build Monte Carlo Inference system. | CO6 |
| 11 | Lab Project |
Practicals Notice 2026 (AIML-A)
AML Exam Notice
AML exam tomorrow will be ORAL.
But you need to write about the given experiment:
- Theory
- Mathematical explanation
- Steps to implement it
To help prepare, use the questions at the end of every experiment.
Experiment 1A & 1B
Build Generalized Linear Model (Classification and Regression)
Experiment 2
Implement Directed Graphical Model (Bayesian Networks)
Experiment 3
Implement Factor Analysis for any real world problem
Experiment 4
Implement EM algorithm for given problem
Experiment 5
Develop a HMM for real world problem
Experiment 6
Implement the Viterbi algorithm to find the most likely sequence of hidden states
Experiment 7
Implement undirected graphical model
Experiment 8
Implement a linear-chain CRF for sequence labeling task