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2017-10-02

-- Practice Midterm 1 Solutions
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-- Practice Midterm 1 Solutions
Solution to Problem 1:
Regression:
Find the best curve of some type (for example, a line) through a set of data points. Then use this curve to predict previous unseen values. For example, might train on predicted claim amounts an insured person might make for certain circumstances, then use this to predict expected new claim amounts.
Transcription: Observe non-textual data and convert it to text. For example, images of street signs.
Denoising: given a corrupted input compute a clean one.
Reference: http://www.cs.sjsu.edu/faculty/pollett/256.1.17f/Lec20170828.html#(14)
Solution to Problem 1: Regression: Find the best curve of some type (for example, a line) through a set of data points. Then use this curve to predict previous unseen values. For example, might train on predicted claim amounts an insured person might make for certain circumstances, then use this to predict expected new claim amounts. Transcription: Observe non-textual data and convert it to text. For example, images of street signs. Denoising: given a corrupted input compute a clean one. Reference: http://www.cs.sjsu.edu/faculty/pollett/256.1.17f/Lec20170828.html#(14)

-- Practice Midterm 1 Solutions
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(Edited: 2017-10-02)
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-- Practice Midterm 1 Solutions
Group: Kunal Deshmukh, Gaurav Gupta, Ujjawal Garg
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(Edited: 2017-10-03)
Group: Kunal Deshmukh, Gaurav Gupta, Ujjawal Garg ((resource:Sol 5_1.jpeg|Resource Description for Sol 5_1.jpeg)) ((resource:Sol 25_2.jpeg|Resource Description for Sol 25_2.jpeg))
2017-10-08

-- Practice Midterm 1 Solutions
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((resource:Screen Shot 2017-10-02 at 7.04.58 PM.JPG|Resource Description for Screen Shot 2017-10-02 at 7.04.58 PM.JPG)) ((resource:Screen Shot 2017-10-02 at 7.06.46 PM.JPG|Resource Description for Screen Shot 2017-10-02 at 7.06.46 PM.JPG)) ((resource:Screen Shot 2017-10-02 at 7.06.51 PM.JPG|Resource Description for Screen Shot 2017-10-02 at 7.06.51 PM.JPG))
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