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I used Russell and Norvig's "Artificial Intelligence: A Modern Approach" as a starting point for the course's schedule and have modified it over the years to he

I used Russell and Norvig's "Artificial Intelligence: A Modern Approach" as a starting point for the course's schedule and have modified it over the years to help align with things I think would continue my students have a broad overview of all of AI. These techniques won't be as widely advertised not because they're inefficient but rather from a marketing perspective it is often easier to just say "AI". However what I would do if I needed to find use case examples would be to search for research articles on Google Scholar [1]. They haven't hooked up a GPT summarizer to Scholar so you'll have to read through everything, but the game is find papers that DID use the approach. For example, [2] is a research paper looking at utilizing Simulated Annealing to schedule disaster relief teams. [1] scholar.google.com [2] https://link.springer.com/article/10.1007/s00500-021-06425-6 https://link.springer.com/article/10.1007/s00500-021-06425-6

rramadass 2y ago
Thank You. I have the Russell & Norvig AIMA book though i have not looked at everything there; probably as you point out it would answer my second question. Any pointers to specific chapters/sections would be appreciated. What i was specifically asking for was for some intuitive and explanatory books on Mathematical Optimization/Programming (https://en.wikipedia.org/wiki/Mathematical_optimization https://en.wikipedia.org/wiki/Mathematical_optimization and https://en.wikipedia.org/wiki/Linear_programming https://en.wikipedia.org/wiki/Linear_programming) independent of AI/ML. This is of course a much older subject with a lot of breadth. However i have not been able to find a intuitive introduction to the subject. Everything has been symbol heavy but not much explanations on the intuitive and model building aspects which imo is crucial to understanding.
- tsumnia 2y ago
  My Constraint Satisfaction lecture (https://www.youtube.com/watch?v=w5tPsbOvkmU https://www.youtube.com/watch?v=w5tPsbOvkmU) may help, though I will say it only serves as a light 75 minute overview of constraints. Specifically in the second half of the lecture I use girl scout cookies and scooters to try and explain Linear Programming. A quick Googling pulled up a Linear Programming Using Excel tutorial: https://science.utm.my/procscimath/wp-content/uploads/sites/605/2021/10/222-231-NorFaiezzatieShahiraBintiMohamad_OngCheeTiong.pdf https://science.utm.my/procscimath/wp-content/uploads/sites/... This type of problem was what helped me understand the optimizations. I haven't studied the inner workings of the Simplex method, but it is used in Excel's Solver as well as Apache Commons'. While it is still computer science oriented, I also like Grokking Algorithms (https://www.manning.com/books/grokking-algorithms https://www.manning.com/books/grokking-algorithms). I used it as a source when I was building out some examples when I was teaching Algorithmic analysis and data structures. It does a great job of distilling the problems into a beginner friendly way that will lead you to dig into "what libraries are out there that have implemented this?"

rramadass 2y ago

Thank You. I have the Russell & Norvig AIMA book though i have not looked at everything there; probably as you point out it would answer my second question. Any pointers to specific chapters/sections would be appreciated. What i was specifically asking for was for some intuitive and explanatory books on Mathematical Optimization/Programming (https://en.wikipedia.org/wiki/Mathematical_optimization https://en.wikipedia.org/wiki/Mathematical_optimization and https://en.wikipedia.org/wiki/Linear_programming https://en.wikipedia.org/wiki/Linear_programming) independent of AI/ML. This is of course a much older subject with a lot of breadth. However i have not been able to find a intuitive introduction to the subject. Everything has been symbol heavy but not much explanations on the intuitive and model building aspects which imo is crucial to understanding.

tsumnia 2y ago
My Constraint Satisfaction lecture (https://www.youtube.com/watch?v=w5tPsbOvkmU https://www.youtube.com/watch?v=w5tPsbOvkmU) may help, though I will say it only serves as a light 75 minute overview of constraints. Specifically in the second half of the lecture I use girl scout cookies and scooters to try and explain Linear Programming. A quick Googling pulled up a Linear Programming Using Excel tutorial: https://science.utm.my/procscimath/wp-content/uploads/sites/605/2021/10/222-231-NorFaiezzatieShahiraBintiMohamad_OngCheeTiong.pdf https://science.utm.my/procscimath/wp-content/uploads/sites/... This type of problem was what helped me understand the optimizations. I haven't studied the inner workings of the Simplex method, but it is used in Excel's Solver as well as Apache Commons'. While it is still computer science oriented, I also like Grokking Algorithms (https://www.manning.com/books/grokking-algorithms https://www.manning.com/books/grokking-algorithms). I used it as a source when I was building out some examples when I was teaching Algorithmic analysis and data structures. It does a great job of distilling the problems into a beginner friendly way that will lead you to dig into "what libraries are out there that have implemented this?"