Unit 1
Introduction to Neuro-Symbolic AI: Definition and overview of Neuro-Symbolic AI- Advantages and disadvantages of Neuro-Symbolic AI- Applications of Neuro-Symbolic AI.
| Course Name | Mathematics for Intelligent Systems 6 |
| Course Code | 23MAT313 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Semester | 6 |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction to Neuro-Symbolic AI: Definition and overview of Neuro-Symbolic AI- Advantages and disadvantages of Neuro-Symbolic AI- Applications of Neuro-Symbolic AI.
Knowledge Representation and Reasoning: Reasoning in neuro-symbolic AI – Types of reasoning. Logical Neural Networks-Markov Random Fields-Hybrid Models
Explainable AI, Multi-Modal Neuro-Symbolic AI, Future Directions in Neuro-Symbolic AI
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Develop intelligent systems using the concept of Neuro-Symbolic AI. |
|
CO2 |
Develop knowledge representation and reasoning techniques in Neuro-Symbolic AI. |
|
CO3 |
Apply the concepts of logical neural networks and Markov random fields in Neuro-Symbolic AI |
|
CO4 |
Develop hybrid models that combine different AI approaches, such as Neuro-Symbolic AI and deep learning |
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 2) |
Internal |
30 |
|
Quizzes (minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term Project/ End Semester Examination |
External |
30 |
Text Books / References
Bouneffouf, Djallel, and Charu C. Aggarwal. “Survey on Applications of Neurosymbolic Artificial Intelligence.” arXiv preprint arXiv:2209.12618 (2022).
Neuro-Symbolic Artificial Intelligence: The Next Big Step” by Daniele Magazzeni and Tomas Petricek.
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