机器学习作业代写 TCSS 478代写

TCSS 478 Project (with multiple due dates)

机器学习作业代写 In particular, we have been discussing machine learning methods and tools that can be used to build predictive models.

In class, we have been learning data science techniques for the analyses of different types of biomedical data.  机器学习作业代写

In particular, we have been discussing machine learning methods and tools that can be used to build predictive models. The goal of this final project is to practice these skills and to contribute what you have learned in real-life collaborative research projects.

In this final project, you will have 2 options:

1. Contribute to a crowd-sourcing challenge called the “ENCODE ImputationChallenge” (https://www.synapse.org/#!Synapse:syn17083203/wiki/).
2. Build ensemble models using Acute Myeloid Leukemia data. This project was initiatedat the NCBI Hackathon in February 2019

(https://github.com/NCBI-Hackathons/ConsensusML).

TCSS 478 Deliverables and Timeline:  机器学习作业代写

• (1 point) Group submission via Canvas: submit names of team membersby 11:59pm of March 4.
• (4 points) No submission: progress report during class on March 6. You must be present and contribute to your group’s progress report to receive full credits. You arenot expected to present any results during this progress report. However, you are expected to inform your instructor which option your group will pursue and to present a plan to divide up the work.
• (10 points) Group submission: slides due on March 11 at 8am. In-classpresentation (ppt, pptx, pdf or google slides) during class in week 10 (March 11 or March 13). You must credit the source and document the division of labor in your team. The order of your presentations will be determined at 8am on March 11. If you choose to pursue Option #2 (AML), you must include a slide that documents what’s new you have contributed on top of the materials already in GitHub.

• (10 points) Group submission: final report due on March 20 at 8am.

You can include any other files that you would like to submit as supporting information. The report should be up to a maximum of 3 pages, with the following sections: Introduction, Methods and Discussions. You can include additional sections or subsections.  If you choose to pursue Option #2 (AML), you must include a section that documents what’s new you have contributed on top of the materials already in GitHub.  机器学习作业代写

• (10 points) Individual submission: code and documentation due on March 20 at 8am. Each student must submit code or Jupyter notebooks for the code that you are primarilyin charge of. You can list additional students who helped with debugging and/or testing. Each of you is expected to contribute as the primary author of part of the code. You must also submit documentation to explain how your code should be run to contribute to the results presented in the final report. You will NOT receive full credits if you are NOT the primary author of any  code.
• (5 points) Individual submission: 1-page educational statement due on March 21at 8am. Each student will submit one page explaining your contributions in this final project and what you have learned. You should also discuss what you wish you have done differently.

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