Unit 1
Introduction to probabilistic graphical models, Probabilistic AI, Introduction to Bayesian Networks, Representation Learning in Bayesian Networks, Inference in Bayesian Networks
| Course Name | Mathematics for Intelligent Systems 5 |
| Course Code | 23MAT303 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Semester | 5 |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction to probabilistic graphical models, Probabilistic AI, Introduction to Bayesian Networks, Representation Learning in Bayesian Networks, Inference in Bayesian Networks
Markov Networks, Independencies in Markov Networks, Hidden Markov Models
Markov Random Fields (MRF), Decision Networks, From Bayesian Networks to Markov Networks
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Model complex systems using the basics of probabilistic graphical models. |
|
CO2 |
Develop a mathematical foundation of Bayesian Networks and their applications in real-world scenarios. |
|
CO3 |
Develop directed and undirected graphical models. |
|
CO4 |
Apply graphical models to real-world problems such as image recognition, natural language processing, and recommendation systems. |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
3 |
3 |
3 |
2 |
3 |
– |
– |
– |
3 |
2 |
– |
3 |
3 |
– |
3 |
|
CO2 |
3 |
3 |
3 |
2 |
3 |
– |
– |
– |
3 |
2 |
– |
3 |
3 |
– |
3 |
|
CO3 |
3 |
3 |
3 |
2 |
3 |
– |
– |
– |
3 |
2 |
– |
3 |
3 |
– |
3 |
|
CO4 |
3 |
3 |
3 |
2 |
3 |
– |
– |
– |
3 |
2 |
– |
3 |
2 |
– |
3 |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (Minimum 3) |
Internal |
30 |
|
Quiz(Minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term project/End semester examination |
External |
30 |
Text Books / References
Artificial Intelligence: A modern Approach’, S J Russell and P Norvig, Pearson (3rd edition), 2010.
‘Machine Learning: A Probabilistic Perspective’, Kevin Murphy and Francis Bach, Penguin Publishers, 2012
Probabilistic graphical models: principles and techniques. Koller, Daphne, and Nir Friedman. MIT press, 2009.
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