Grade statistics for Machine Learning modules 2021-22
Dear University College London,
I would like to request information on the distribution of marks (specifically: 5, 25, 50, 75, 90, 95 and 99 percentiles) as well as the number of students taking the module for the 2021/2022 cohort for the following modules:
- Supervised Learning (COMP0078)
- Graphical Models (COMP0080)
- Applied Machine Learning (COMP0081)
- Bioinformatics (COMP0082)
- Advanced Topics in Machine Learning (COMP0083)
- Information Retrieval and Data Mining (COMP0084)
- Approximate Inference and Learning in Probabilistic Models (COMP0085)
- Probabilistic and Unsupervised Learning (COMP0086)
- Statistical Natural Language Processing (COMP0087)
- Introduction to Machine Learning (COMP0088)
- Reinforcement Learning (COMP0089)
- Introduction to Deep Learning (COMP0090)
- Machine Vision (COMP0137)
- Machine Learning Seminar (COMP0168)
- Bayesian Deep Learning (COMP0171)
- AI for Biomedicine and Healthcare (COMP0172)
- AI for Sustainable Development (COMP0173)
- Statistical Learning Theory (COMP0175)
Where students are awaiting resits or late summer assessments please specify the number of such students and exclude their grades in the summary statistics.
Yours faithfully,
Daniel Liu
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Dear University College London,
Further to my previous request, could you kindly provide the same information for:
Financial Engineering (COMP0048)
Algorithmic Trading (COMP0051)
Digital Finance (COMP0164)
Introduction to Deep Learning (COMP0090)
Forecasting (STAT0010)
Decision and Risk (STAT0011)
Selected Topics in Statistics (STAT0017)
Statistical Models and Data Analysis (STAT0028)
Statistical Design of Investigations (STAT0029)
Statistical Computing (STAT0030)
Applied Bayesian Methods (STAT0031)
Introduction to Statistical Data Science (STAT0032)
Yours faithfully,
Daniel Liu
Dear Daniel
Thank you for your Freedom of Information request of 7^th August 2022.
We have completed the compilation of information in response to your
request for information about the distribution of marks (specifically the
5^th, 25^th, 50^th, 75^th, 90^th, 95^th and 99^th percentiles) for the
following modules, as well as the number of students taking the module for
the 2021/22 cohort:
• Supervised Learning (COMP0078)
• Graphical Models (COMP0080)
• Applied Machine Learning (COMP0081)
• Bioinformatics (COMP0082)
• Advanced Topics in Machine Learning (COMP0083)
• Information Retrieval and Data Mining (COMP0084)
• Approximate Inference and Learning in Probabilistic Models (COMP0085)
• Probabilistic and Unsupervised Learning (COMP0086)
• Statistical Natural Language Processing (COMP0087)
• Introduction to Machine Learning (COMP0088)
• Reinforcement Learning (COMP0089)
• Introduction to Deep Learning (COMP0090)
• Machine Vision (COMP0137)
• Machine Learning Seminar (COMP0168)
• Bayesian Deep Learning (COMP0171)
• AI for Biomedicine and Healthcare (COMP0172)
• AI for Sustainable Development (COMP0173)
• Statistical Learning Theory (COMP0175)
• Financial Engineering (COMP0048)
• Algorithmic Trading (COMP0051)
• Digital Finance (COMP0164)
• Introduction to Deep Learning (COMP0090)
• Forecasting (STAT0010)
• Decision and Risk (STAT0011)
• Selected Topics in Statistics (STAT0017)
• Statistical Models and Data Analysis (STAT0028)
• Statistical Design of Investigations (STAT0029)
• Statistical Computing (STAT0030)
• Applied Bayesian Methods (STAT0031)
• Introduction to Statistical Data Science (STAT0032)
Where students are awaiting resits or late summer assessments, please
specify the number of such students and exclude their grades in the
summary statistics.
We can confirm that we do hold some information of the description
specified in your request and this information is provided in the attached
spreadsheet.
As you will see within the attached spreadsheet, where the number of
candidates resitting or taking late summer assessments is five or fewer,
the information has been withheld (represented as ≤5). In addition, we
have withheld module percentiles where these represent a group of 22 or
fewer students (represented as ≤22). This information has been withheld
because, with such low numbers, even providing this information on an
anonymised basis there is a risk the individuals may be identifiable,
which means that these are personal data of third parties, or if linked
with other personal identifiers in the public domain, would be likely to
become personal data. This information has been withheld under Section
40(2) of the Freedom of Information Act 2000 (FOIA) by virtue of Section
40(3A)(i).
Section 40(2) of the FOIA allows a public authority to withhold
information under the FOIA where (i) the requested information is personal
data relating to someone other than the requester, and (ii) its disclosure
would breach any of the Data Protection principles. In this case, we
believe that the requested information could relate to and identify
individuals and therefore would be personal data. The disclosure of these
personal data would not be within the reasonable expectations of the
individuals concerned and it would be unfair to do so; this therefore
breaches the first Data Protection principle.
As the Section 40(2) exemption is an absolute one, there is no need to
conduct a public interest test.
You are free to use any information supplied for your own use, including
for non-commercial research purposes. The information may also be used for
the purposes of news reporting. However, any other type of re-use, for
example by publishing or issuing copies to the public, will require the
permission of the copyright owner.
If you are unhappy with our response to your request and wish to make a
complaint or request a review of our decision, please email
[1][email address]. Emails should include the words ‘Internal
Review’ in the subject and be marked For the Attention of the
Vice-President (Operations), alternatively you should write to:
Vice-President (Operations)
University College London
Gower Street
London
WC1E 6BT
Please note, complaints and requests for internal review received more
than two months after the initial decision will not be handled.
If you are not content with the outcome of the internal review, you may
apply directly to the Information Commissioner at the address given below.
You should do this within two months of our final decision.
If you have any queries or concerns, please contact me using the details
provided in this letter and including the request reference number.
Further information on the Freedom of Information Act is available from
the Information Commissioner’s Office:
Wycliffe House
Water Lane
Wilmslow
SK9 5AF
01625 545700
[2]www.ico.org.uk
[3][email address]
Kind regards,
Janine Small
Data Protection and Freedom of Information Adviser
University College London
Legal Services
Data Protection: [4][email address] FOI: [5][UCL request email]
References
Visible links
1. mailto:[email address]
2. http://www.ico.org.uk/
3. mailto:[email address]
4. mailto:[email address]
5. mailto:[UCL request email]
Dear University College London,
Thank you for the response. I believe there is a misunderstanding in my request and I would like to clarify:
I am requesting the grade statistics for students who took the exam in NORMAL (April to May) examination period, rather than LSA. This includes Level 6 and 7. I am also requesting the number of students who are taking LSA for these modules.
I hope to receive a spreadsheet detailing the above information, in all the modules I listed in previous replies.
Yours faithfully,
Daniel Liu
Dear Daniel
Thank you for your Freedom of Information request of 23^rd August 2022.
We have completed the compilation of information in response to your
request for the following corrected information following our response to
your previous request attached (Ref: FOI 022-534):
I am requesting the grade statistics for students who took the exam in
NORMAL (April to May) examination period, rather than LSA. This includes
Level 6 and 7. I am also requesting the number of students who are taking
LSA for these modules.
We can confirm that we do hold some information of the description
specified in your request and this information is provided in the attached
spreadsheet.
As you will see within the attached spreadsheet, where the number of
candidates resitting or taking late summer assessments is five or fewer,
the information has been withheld (represented as ˜5). In addition, we
have withheld module percentiles where these represent a group of 22 or
fewer students (represented as ˜22). This information has been withheld
because, with such low numbers, even providing this information on an
anonymised basis there is a risk the individuals may be identifiable,
which means that these are personal data of third parties, or if linked
with other personal identifiers in the public domain, would be likely to
become personal data. This information has been withheld under Section
40(2) of the Freedom of Information Act 2000 (FOIA) by virtue of Section
40(3A)(i).
Section 40(2) of the FOIA allows a public authority to withhold
information under the FOIA where (i) the requested information is personal
data relating to someone other than the requester, and (ii) its disclosure
would breach any of the Data Protection principles. In this case, we
believe that the requested information could relate to and identify
individuals and therefore would be personal data. The disclosure of these
personal data would not be within the reasonable expectations of the
individuals concerned and it would be unfair to do so; this therefore
breaches the first Data Protection principle.
As the Section 40(2) exemption is an absolute one, there is no need to
conduct a public interest test.
You are free to use any information supplied for your own use, including
for non-commercial research purposes. The information may also be used for
the purposes of news reporting. However, any other type of re-use, for
example by publishing or issuing copies to the public, will require the
permission of the copyright owner.
If you are unhappy with our response to your request and wish to make a
complaint or request a review of our decision, please email
[1][email address]. Emails should include the words ‘Internal
Review’ in the subject and be marked For the Attention of the
Vice-President (Operations), alternatively you should write to:
Vice-President (Operations)
University College London
Gower Street
London
WC1E 6BT
Please note, complaints and requests for internal review received more
than two months after the initial decision will not be handled.
If you are not content with the outcome of the internal review, you may
apply directly to the Information Commissioner at the address given below.
You should do this within two months of our final decision.
If you have any queries or concerns, please contact me using the details
provided in this letter and including the request reference number.
Further information on the Freedom of Information Act is available from
the Information Commissioner’s Office:
Wycliffe House
Water Lane
Wilmslow
SK9 5AF
01625 545700
[2]www.ico.org.uk
[3][email address]
Kind regards,
Janine Small
Data Protection and Freedom of Information Adviser
University College London
Legal Services
Data Protection: [4][email address] FOI: [5][UCL request email]
References
Visible links
1. mailto:[email address]
2. http://www.ico.org.uk/
3. mailto:[email address]
4. mailto:[email address]
5. mailto:[UCL request email]
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