-
About The Course
This Course is Only Available On Our App
Dive-in and start learning! Get offline access to all the course contents!
No items in this section -
Description
This course enables learning on different graph traversal techniques (BFS & DFS)
along with enhanced search algorithms like A* algorithm. Genetic algorithms are discussed along with Min-Max algorithms.
Expert systems and ANN are also discussed in detail along with Fuzzy logic in SC.
Along with a pdf with important notes and explanations
Modules Covered:
Introduction to AI / SC
Problem solving algorithms
Knowledge, Reasoning and Planning.
Fuzzy Logic.
Artificial Neural Network.
Expert System. -
How to Pass AISC
-
Introduction to Artificial Intelligence(AI) and Soft Computing
-
Problem Solving
- BFS ( Breadth First Search ) Algorithm with solved Example
- DFS ( Depth First Search ) Algorithm with solved Example
- IDFS ( Iterative Depth First Search ) Algorithm with solved Example
- GBFS Solved Example
- A Star solved Example
- Hill Climbing
- Min Max Solved Example
- Alpha-Beta Pruning Solved Example
- Genetic Algorithm
- Genetic Algorithm Max one Problem Solved Example
-
Fuzzy Logic
-
Knowledge, Reasoning and Planning
-
Artificial Neural Network
-
Expert System
-
Notes
-
Extra Notes
- Introduction to Artificial Intelligence and Soft Computing (Module 1 Notes)
- Artificial Intelligence Notes #1
- Artificial Intelligence Notes #2
- Soft Computing Module 4
- Soft Computing Module 5
- Soft Computing Handmade Notes
- Artificial Intelligence and Soft Computing Complete Notes ( Toppers Solution )
- Mobile Communication and Computing Notes ( Toppers Solution )
- Digital Signal Processing Handmade Notes
Soft computing vs Hard computing and Supervised learning vs Unsupervised Learning
Soft computing vs Hard computing and Supervised learning vs Unsupervised Learning
Soft Computing relies on formal logic and probabilistic reasoning. Hard computing relies on binary logic and crisp system.Soft computing works on ambiguous and noisy data. Hard computing works on exact data. Hard computing is best for solving the mathematical problems which don’t solve the problems of the real world. Soft computing is better used in solving real-world problems as it is stochastic in nature i.e., it is a randomly defined process that can be analyzed statistically but not with precision. The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not. Unsupervised learning models, in contrast, work on their own to discover the inherent structure of unlabeled data.
Prepare For Your Placements: https://lastmomenttuitions.com/courses/placement-preparation/
/ Youtube Channel: https://www.youtube.com/channel/UCGFNZxMqKLsqWERX_N2f08Q
Follow For Latest Updates, Study Tips & More Content!
