Problem 1 – Learning from Examples and Probabilistic Models ________ out of 45 points
· Select a learning problem that may arise in the real world. Examples may arise from various domains incorporating examples and probabilistic models, including Naïve Bayes, classifiers, decision trees, clustering, etc. ______ out of 5 points
· Describe the learning problem(s) used (based on methods discussed in class or in the book, including Naïve Bayes, classifiers, decision trees, clustering, etc.) ______ out of 5 points
· Design and describe the algorithm and approach of your implementation. Since this is a not a machine learning course, you should describe your general approach to a machine learning problem. You may create visual representations and include a description as well. ______ out of 10 points
o Your general approach to the learning problem that you have selected should be described in terms of model selection, optimization, and which parameters may require adjustment, etc.
o You may show pseudocode or a visual representation of your chosen problem.
o Any code used from other sources should be referenced. Implementing the code from scratch can help show that you have acquired a solid understanding of how it should operate.
o Please include references for your sources.
· Explain why you selected your chosen method(s). ______ out of 10 points
o Explain how your chosen method would help solve your chosen problem.
o Think about the how the problem/application is formulated, model representations used, algorithms for representing problems, how your solution may be optimized, etc. Your project may integrate various approaches pertaining to machine learning.
· Describe the strengths and limitations. ______ out of 5 points
· Describe what may have been some alternative methods. ______ out of 5 points
Describe what methods may not have been appropriate ______ out of 5 points
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