Curriculum
- 11 Sections
- 55 Lessons
- 26 Weeks
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- About The Course.course-payment { float: right; display: inline-block; position: relative; margin-bottom: 40px; display: none; } /*! CSS Used from: Embedded */ *, ::after, ::before { box-sizing: border-box; border-width: 0; border-style: solid; border-color: #e5e7eb; } ::after, ::before { --tw-content: ''; } h2 { font-size: inherit; font-weight: inherit; } a { color: inherit; text-decoration: inherit; } h2, p { margin: 0; } :disabled { cursor: default; } *, ::before, ::after { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; } .mx-auto { margin-left: auto; margin-right: auto; } .mb-2 { margin-bottom: 0.5rem; } .mb-4 { margin-bottom: 1rem; } .mb-6 { margin-bottom: 1.5rem; } .mr-2 { margin-right: 0.5rem; } .max-w-screen-sm { max-width: 640px; } .max-w-screen-xl { max-width: 1280px; } .rounded-lg { border-radius: 0.5rem; } .bg-primary-700 { --tw-bg-opacity: 1; background-color: rgb(29 78 216 / var(--tw-bg-opacity)); } .bg-white { --tw-bg-opacity: 1; background-color: rgb(255 255 255 / var(--tw-bg-opacity)); } .px-4 { padding-left: 1rem; padding-right: 1rem; } .px-5 { padding-left: 1.25rem; padding-right: 1.25rem; } .py-2.5 { padding-top: 0.625rem; padding-bottom: 0.625rem; } .py-8 { padding-top: 2rem; padding-bottom: 2rem; } .text-center { text-align: center; } .text-4xl { font-size: 3rem; line-height: 2.5rem; } .text-sm { font-size: 0.875rem; line-height: 1.25rem; } .font-extrabold { font-weight: 800; } .font-light { font-weight: 300; } .font-medium { font-weight: 500; } .leading-tight { line-height: 1.25; } .tracking-tight { letter-spacing: -0.025em; } .text-gray-500 { --tw-text-opacity: 1; color: rgb(107 114 128 / var(--tw-text-opacity)); } .text-gray-900 { --tw-text-opacity: 1; color: rgb(17 24 39 / var(--tw-text-opacity)); } .text-white { --tw-text-opacity: 1; color: rgb(255 255 255 / var(--tw-text-opacity)); } .hover\:bg-primary-800:hover { --tw-bg-opacity: 1; background-color: rgb(30 64 175 / var(--tw-bg-opacity)); } .focus\:outline-none:focus { outline: 2px solid transparent; outline-offset: 2px; } .focus\:ring-4:focus { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(4px + var(--tw-ring-offset-width)) var(--tw-ring-color); box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); } .focus\:ring-primary-300:focus { --tw-ring-opacity: 1; --tw-ring-color: rgb(147 197 253 / var(--tw-ring-opacity)); } @media (min-width: 640px) { .sm\:py-16 { padding-top: 4rem; padding-bottom: 4rem; } } @media (min-width: 768px) { .md\:text-lg { font-size: 1.5rem; line-height: 1.75rem; } } @media (min-width: 1024px) { .lg\:px-6 { padding-left: 1.5rem; padding-right: 1.5rem; } } .imgdata { width: 35% } @media (max-width: 767px) { .imgdata { width: 40% } }0
- DescriptionThis 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.1 - How to Pass AISC1
- Introduction to Artificial Intelligence(AI) and Soft Computing5
- Problem Solving10
- 5.1BFS ( Breadth First Search ) Algorithm with solved Example5 Minutes
- 5.2DFS ( Depth First Search ) Algorithm with solved Example3 Minutes
- 5.3IDFS ( Iterative Depth First Search ) Algorithm with solved Example3 Minutes
- 5.4GBFS Solved Example7 Minutes
- 5.5A Star solved Example13 Minutes
- 5.6Hill Climbing4 Minutes
- 5.7Min Max Solved Example6 Minutes
- 5.8Alpha-Beta Pruning Solved Example13 Minutes
- 5.9Genetic Algorithm5 Minutes
- 5.10Genetic Algorithm Max one Problem Solved Example8 Minutes
- Fuzzy Logic6
- 6.1Introduction to Fuzzy Logic4 Minutes
- 6.2Fuzzification and De-Fuzzification6 Minutes
- 6.3Properties and Operation of Crisp and Fuzzy Sets5 Minutes
- 6.4Crisp and Fuzzy Sets and Relations11 Minutes
- 6.5Fuzzy Membership Function8 Minutes
- 6.6Mamdani Fuzzy Model (Fuzzy Controller) with Solved Example33 Minutes
- Knowledge, Reasoning and Planning6
- Artificial Neural Network7
- 8.1Introduction to ANN and structure of ANN6 Minutes
- 8.2Mc-Culloch-Pitts Neural Model3 Minutes
- 8.3Neural Network Architecture5 Minutes
- 8.4Perceptron Learning (with solved example)11 Minutes
- 8.5Activation functions in ANN (Discrete and Continuous)4 Minutes
- 8.6Backpropagation Network (with solved example)19 Minutes
- 8.7Self Organizing Maps and KSOMs10 Minutes
- Expert System4
- Notes6
- Extra Notes9
- 11.1Introduction to Artificial Intelligence and Soft Computing (Module 1 Notes)
- 11.2Artificial Intelligence Notes #1
- 11.3Artificial Intelligence Notes #2
- 11.4Soft Computing Module 4
- 11.5Soft Computing Module 5
- 11.6Soft Computing Handmade Notes
- 11.7Artificial Intelligence and Soft Computing Complete Notes ( Toppers Solution )
- 11.8Mobile Communication and Computing Notes ( Toppers Solution )
- 11.9Digital 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.
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