Mini Project 5 –Learning and Deep Learning

This project reinforces the idea that learning from examples and probabilistic models and deep learning approaches are useful procedures in AI and may be specialized for different kinds of use cases. Such methods permit agents to improve their behavior by learning from past experiences and making future predictions, and to develop probabilistic models based on uncertain reasoning from observations. These learning strategies are especially of interest in the development of intelligent agents, game theory, DNA sequence analysis, and autonomous robots, among many other applications. This assignment provides students a taste of the complexity of human behavior and encourages them to study real life examples and engage in self-reflection, both common practices by researchers developing new AI techniques. Here you will explore a case of using learning from examples and probabilistic models, and another case exploring use of deep learning.

Problem 1 – Learning from Examples and Probabilistic Models ________ out of 45 points

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· 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

Problem 2 – Deep Learning ________ out of 45 points

· Select a problem using deep learning that may arise in the real world. Examples may arise from various domains incorporating deep learning in fields such as computer vision, natural language processing, robotics, etc. ______ out of 5 points

· Describe the deep learning problem(s) used (based on methods discussed in class or in the book, including feedforward networks and computation graphs, convolutional neural networks, recurrent neural networks, etc.) ______ out of 5 points

· Design and describe the algorithm and approach of your implementation. Since this is a not a deep learning course, you should describe your general approach to a deep learning problem. You may create visual representations and include a description as well. ______ out of 10 points

o Your general approach to the deep learning problem that you have selected should be described in terms of construction of the neural network with input, output and hidden layers, use of appropriate activation functions, loss 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 deep 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

Overall Project Review and Reflection ______ out of 10 points

· Describe what you learned from this assignment, what could be improved, and what could be some future extensions of your work.

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