Questionnaire for CSE 291 B00 NLP
Please answer all the questions honestly.
([Acknowledgement]: questions largely based on Prof. Arun Kumar's 291 Winter 2017 enrollment questions)
Full name *
UCSD student ID *
Official UCSD email *
Degree program *
Current year of degree at UCSD *
If MS, what is the concentration area? *
Have you taken an NLP course before? *
Where did you take the NLP course and what was your grade?
Which of the following PLs are you proficient in (select all that apply)? *
Required
Which of the following ML libraries/tools are you proficient in or have used before(select all that apply)? *
Required
What is your dream career? *
Why do you want to take this course (select all that apply)?
Have you done a research project on a topic relevant to this course? If yes, briefly describe(<10 sentences) the project's goal, problem, your contributions, and outcome. Also mention the name of your project advisor/supervisor *
Have you published a research paper on a topic relevant to this course? If yes, provide the citation for your paper (title, author list, venue, year) and if available, a URL *
Which of the following aspects of working on a research problem excite you the most (select all that apply; preferably not more than 3)? *
Required
Read the following paper and briefly summarize the main contributions of the paper, in your own words (<10 sentences) http://www.aclweb.org/anthology/N13-1008 *
Briefly list 3 strong points and 3 technical limitations of the Riedel 2013 paper along with how you think each limitation can be addressed (<5 sentences for each point): http://www.aclweb.org/anthology/N13-1008 *
It is important to explore deep learning-based solutions to all data analysis applications due to possibly higher accuracy and less manual feature engineering, even at the cost of interpretability or theoretical understanding offered by other ML techniques. Agree or disagree? Justify your opinion (<10 sentences). *
List and briefly justify 3 grand open research challenges that, in your view, Artificial Intelligence (AI) should and can solve in the next 10 years (<5 sentences each). *
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