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Your Final Project will have 3 Tasks as described below:
Deliverable 1 (project proposal due on October 19) – Project topic and one-page summary of the project proposal to be submitted on Blackboard.
Deliverable 2 (project progress due on November 9) – two-pages describing projects’ status, results/outcomes obtained, timeline, and next steps.
Deliverable 3 (final presentation and project report due on December 7) – 15 to 20-minutes recorded presentation, using Microsoft Stream, outlining the project, deliverables, literature search, results, and potential future work.
You will research AI tools and technologies. Your plan would aimed toward the utilization, advancement, or leveraging of AI, with the ultimate goal to transform one or more aspects. At the end of the course, you will submit a video multi-slide presentation to the below specifications for one AI-based opportunity. 
The following template describes the various elements that your Final project and presentation can cover.
1) Summarize the AI application in 2-3 sentences.
Focus on the problem.
How is your application going to generate business value?
Does this application create/help you maintain competitive advantage? How?
2) Strategy: How is your application going to generate business value?
Describe if it is an improvement over an existing technology or it solves an entirely new problem.
Briefly describe the technology to the extent which we covered in class (e.g.,Supervised/ Unsupervised/Reinforcement learning, whether it involves neural networks, vision, speech).
3) What metrics are you going to use to evaluate the success of your application?
Describe how this metric is connected to the underlying problem need.
Describe how you would interpret particular values of this metric with respect to actionability (e.g., what action would you take if your metric rose above some threshold value or below some floor?).
4) Technology: What AI/ML technique are you going to use for your application?
Are you going to adapt a readily available algorithm, or do you have to develop your own?
How are you going to train the algorithm, and what are the training objectives?
5) Data: Most of AI applications are data hungry.
How about yours? Describe how you will use large datasets, or why your approach does not need large data.
How are you going to get the data? Do you already have access to it?
Who owns the data? Where does the data reside in your information systems?
If you don’t have the data yet, how are you going to obtain/ generate it?
To the degree that integration is required, how will you link data across systems?
6) Humans and AI: What organizational and human factors are crucial for the success of your case?
Is your application financially viable? If it requires investment, provide a rough estimate of the expected ROI.
Describe any organizational change or acceptance that your project necessitates.
How are you going to make the above happen?
Does your AI application interact with humans within or outside the organization?What steps are required to make the interaction smooth?
Discuss any potential moral or ethical implications that your project may have.
7) Experimental results and analysis using Jupyter Notebook and project presentation as a video content.
List of AI Tools and Frameworks
Scikit Learn
Auto ML
H20: Open Source AI Platform
Google ML Kit


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