With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more.
In this course, you will understand what artificial intelligence is. It explains in detail basic search methods: DFS (Depth First Search), BFS (Breadth First Search), and A* search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take.
About The Author :
Devangini Patel is a Ph.D. student at the National University of Singapore, Singapore. Her research interests include Deep Learning, Computer Vision, Machine Learning and Artificial Intelligence. She has completed masters in Artificial Intelligence from University of Southampton, UK. She has over 5 years of experience in the field of AI and has worked on various industrial and research projects in AI including facial expression analysis, robotics, virtual try-on, object recognition and detection and advertisement ranking.
Build a sample search tree by answering the questions.
Understand graph structure for this application by answering the questions
Compare both the algorithms.
Explain an application that you can develop yourself.
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