[Stanford]Machine Learning Week #6 作业讨论帖

X. Advice for Applying Machine Learning Help

Attempt Review Question
Due Date        Sun 10 Aug 2014 11:59 PM PDT
If you submit after the due date (but before the hard deadline), your submission score will be penalized 20%.

Hard Deadline        Sun 14 Sep 2014 11:59 PM PDT
If you submit any time after the hard deadline, you will not receive credit.

Effective Score        4.75 / 5.00
Explanation: 4.75 = 4.75 (Score for attempt 2) * 100% (No penalties)
Each time that you attempt it, we'll record a score based on your performance and any penalties due to late submissions. Your effective score will be the highest score of all the allowed attempts made before the hard deadline.

# of Attempts        2 / 100
Last Attempted        Sun 27 Jul 2014 8:22 AM PDT
Last Attempted Score        4.75 / 5.00Show Previous Attempts
  XI. Machine Learning System Design (Week 6)
(expanded, click to collapse)
Completed
XI. Machine Learning System Design Help

Attempt Review Question
Due Date        Sun 10 Aug 2014 11:59 PM PDT
If you submit after the due date (but before the hard deadline), your submission score will be penalized 20%.

Hard Deadline        Sun 14 Sep 2014 11:59 PM PDT
If you submit any time after the hard deadline, you will not receive credit.

Effective Score        4.50 / 5.00
Explanation: 4.50 = 4.50 (Score for attempt 2) * 100% (No penalties)
Each time that you attempt it, we'll record a score based on your performance and any penalties due to late submissions. Your effective score will be the highest score of all the allowed attempts made before the hard deadline.

# of Attempts        2 / 100
Last Attempted        Sun 27 Jul 2014 8:48 AM PDT
Last Attempted Score        4.50 / 5.00
Regularized Linear Regression and Bias/Variance Help

View Instructions
Due Date        Sun 10 Aug 2014 11:59 PM PDT
If you submit after the due date (but before the hard deadline), your submission score will be penalized 20%.

Hard Deadline        Sun 14 Sep 2014 11:59 PM PDT
If you submit any time after the hard deadline, you will not receive credit.

Part        Name        Last Submission        Score        Feedback       
1 / 5        Regularized Linear Regression Cost Function        Sun 27 Jul 2014 7:25 PM PDT        25.00 / 25        View       
2 / 5        Regularized Linear Regression Gradient        Sun 27 Jul 2014 7:25 PM PDT        25.00 / 25        View       
3 / 5        Learning Curve        Sun 27 Jul 2014 7:35 PM PDT        20.00 / 20        View       
4 / 5        Polynomial Feature Mapping        Sun 27 Jul 2014 7:49 PM PDT        0.00 / 10        View       
5 / 5        Cross Validation Curve        Sun 27 Jul 2014 8:09 PM PDT        20.00 / 20        View       
Total Score        90 / 100

一口气交到今天的作业

剩下的附件限制传不上图了...直接贴字好了

X. Advice for Applying Machine Learning Help

Attempt Quiz
Due Date        Sun 10 Aug 2014 11:59 PM PDT
If you submit after the due date (but before the hard deadline), your submission score will be penalized 20%.

Hard Deadline        Sun 14 Sep 2014 11:59 PM PDT
If you submit any time after the hard deadline, you will not receive credit.

Effective Score        5.00 / 5.00
Explanation: 5.00 = 5.00 (Score for attempt 3) * 100% (No penalties)
Each time that you attempt it, we'll record a score based on your performance and any penalties due to late submissions. Your effective score will be the highest score of all the allowed attempts made before the hard deadline.

# of Attempts        3 / 100
Last Attempted        Sun 10 Aug 2014 9:17 PM PDT
Last Attempted Score        5.00 / 5.00


XI. Machine Learning System Design Help

Attempt Quiz
Due Date        Sun 10 Aug 2014 11:59 PM PDT
If you submit after the due date (but before the hard deadline), your submission score will be penalized 20%.

Hard Deadline        Sun 14 Sep 2014 11:59 PM PDT
If you submit any time after the hard deadline, you will not receive credit.

Effective Score        4.50 / 5.00
Explanation: 4.50 = 4.50 (Score for attempt 3) * 100% (No penalties)
Each time that you attempt it, we'll record a score based on your performance and any penalties due to late submissions. Your effective score will be the highest score of all the allowed attempts made before the hard deadline.

# of Attempts        3 / 100
Last Attempted        Sun 10 Aug 2014 10:50 PM PDT
Last Attempted Score        4.50 / 5.00

Mycroft 发表于 2014-8-3 00:54
来交作业了

求问，我learning curve那一道题不知道哪里错了？
for i = 1:m
        [theta] = trainLinearReg([ones(i, 1) X(1:i,:)], y(1:i), 0);
        error_train(i) = 1/(2*i) * sum(([ones(i, 1) X(1:i,:)]*theta - y(1:i)).^2);
        error_val(i) = 1/(2*length(yval)) * sum(([ones(length(yval),1) Xval]*theta - yval).^2);
end

交作业~

交作业

膜拜斯坦福大神！

交作业咯

交作业啦

交作业啦

Time to hand in homework!

---

交作业~~~

交作业咯