Coursera course on Machine Learning

Last autumn, Stanford for the first time organized a MOOC (massive open online course) on machine learning ( http://www.ml-class.org ). The course has since become part of the offering of Coursera ( https://www.coursera.org/course/ml ) and seems to be offered at least a few times per year. (For instance, there is currently an on-going course.) 

Similarly to last year, during this academic year (i.e. autumn term 2012 and spring term 2013), it will again be possible to obtain credits for taking the above course, and this can be used as a substitute for our department's own local course on the same topic, called Introduction to Machine Learning ( http://www.cs.helsinki.fi/en/courses/582631/2012/s/k/1 ).

The rules for obtaining credits are as follows:
 
1. Credits will be awarded if and only if *both* of the following are satisfied:
 
   a) The student submits to the lecturer ( http://www.cs.helsinki.fi/u/phoyer/contact.html ), 
      at least 1 week before an exam, by email a package containing the following:
      - Full name and student number (in the email), and
      - The statement of accomplishment awarded by Coursera (pdf file), and
      - A brief report (2-3 pages) (as a pdf file) that in your own words summarizes your 
        experience of the Coursera course. This can be written in Finnish, 
        Swedish, or English. You should include at least:
        * what your initial skill level was
        * how difficult and how time-consuming the course was (how many hours per week)
        * what do you feel that you learned (any specific topics or techniques?)
        * are you happy with your experience with the Coursera course?
        * for those who have also taken the local Introduction to Machine Learning course, 
          some indication of how the Stanford course compare in terms of difficulty, in terms 
          of the amount of work needed, and in terms of how much you learned
 
   b) The student takes part in, and passes, the written exam covering the topics of the 
      Coursera course.
      
   Note that these exams run parallel with the exams for the local Introduction to Machine 
   Learning course exams. I.e. please sign up for the exam using the regular department 
   system, and arrive at the same time and to the same room for the exam. The exam
   questions, however, are distinct: The questions for students who are applying for 
   credit for the Coursera course are different from the questions for the students who
   are taking the exam for the local course.
 
2. All credits for the Coursera course are graded simply pass/fail.
 
3. The same number of credits (4 credit units) are awarded for taking the Coursera course as
   for taking the local course, and they are substitutes in terms of the degree programme. If 
   a student completes *both* the Coursera course and our local course, a total of 6 credit
   units is awarded for the combination.
 
4. There are no guarantees that any other MOOC courses on this topic will be accepted for credit. 
   The Coursera course is the recommended one, at least for this academic year.
 
5. The above rules apply for exams in autumn 2012 and spring 2013. There are no guarantees that
   the same will apply in years to come.
 
Note that the CS Department has no additional information nor ways to influence the Coursera course. We also cannot provide additional support to students participating in the class, so students are expected to rely on the various support options provided by the instructors (at Coursera) running the class.
 
20.09.2012 - 15:48 Patrik O Hoyer
20.09.2012 - 15:48 Patrik O Hoyer