Knowledge Representation in Artificial Intelligence – MITU Skillologies – Aritificial Intelligence, Data Science Training and Development

This page is dedicated for study material of MCA subject “Knowledge Representation in Artificial Intelligence”

1. Introduction to AI

2. How AI is affecting on real life?

3. Different Branches of AI

4. Advantages and Disadvantages of AI

5. Knowledge Representation in AI

6. AND-OR Graph

7. Mathematical Logic and Inference

8. First Order Logic

9. Forward Chaining and Backward Chaining

10. Language of Propositional Logic

12. Valid Arguments and Proof Systems

13. Rules of Inference and Natural Deduction

14. Hilbert Systems

15. Tableau Method

16. The Resolution Refutation Method

17. Machine Learning

19. Least Square Regression

20. Classification

21. Logistic Regression

22. Decision Tree

23. Random Forest and Ensembling

24. Support Vector Machine

25. Bayesian Classification

26. K-Nearest Neighbor

27. K-means Clustering

28. Association Rule Mining

29. Data Analytics

30. Frameworks of Data Analytics

31. Introduction to Deep Learning

33. Multi-Layer Perceptron

34. Building the first neural network

35. Convolutional Neural Network

36. Recurrent Neural Network

37. Generative Advesarial Network

38. Natural Language Processing

39. Hardware and Software for AI

40. Hardware and Software for AI – FPGA

41. Hardware and Software for AI – Gateway Edge

42. Hardware and Software for AI – GPU

43. Hardware and Software for AI – CPU and GPU

44. Applications of AI – Chatbot

45. Applications of AI – NLP

46. Applications of AI – Image Processing

47. Applications of AI – Speech Recognition

Download the Programs of Python in Jupyter Notebook from this link

The Dataset location of these programs