Studiewijzer 2022/2023
Globale inhoud
Modern machine learning methods are based on mathematical concepts, especially from probability theory and statistics. This course treats these concepts in detail, through the spectrum of the Bayesian school of thought in machine learning. This will lay the groundwork for a solid understanding of advanced machine learning methods taught in other courses. Additionally, the mathematical theory will be made more concrete through programming exercises.
Leerdoelen
- Understand the frequentist and Bayesian statistical frameworks
- Understand sample spaces and basic probability measures built on them
- Be able to manipulate probabilistic expressions using the laws of probability
- Understand and manipulate random variables (expectations, means, variances, etc.)
- Derive formulas for probability distributions (in particular including posterior, predictive, and marginal likelihood distributions in Bayesian statistics) using the operations of marginalization and conditioning
- Be comfortable deriving and manipulating conjugate distributions
- Interpret the implications of properties of the prior, posterior and predictive distributions
- Understand parameter estimation techniques based on maximum likelihood and maximum a posteriori objectives.
- Be able to derive maximum likelihood and maximum a posteriori estimators.
- Be able to compare models in the Bayesian framework
Onderwijsvormen
Verdeling leeractiviteiten
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Activiteit
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Uren
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Deeltoets
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4
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Hoorcollege
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24
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Werkcollege
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24
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Zelfstudie
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116
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Totaal
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168
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(6 EC x 28 uur)
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Aanwezigheid
Aanwezigheidseisen opleiding (OER-B):
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Voor practica en werkgroepbijeenkomsten met opdrachten geldt een aanwezigheidsplicht. De invulling van deze aanwezigheidsplicht kan per vak verschillen en staat aangegeven in de studiewijzer. Wanneer studenten niet voldoen aan deze aanwezigheidsplicht kan het onderdeel niet met een voldoende worden afgerond.
Aanvullende eisen voor dit vak:
Attendance is not formally required but strongly encouraged. During class hours (hoorcollege), in-class exercises will be completed and discussed, which are graded pass/fail and count for 15% of the overall grade. During recitations (werkcollege), some number of homework problems will be explained each week. Homework assignments count for 35% of the overall grade.
Toetsing
| Onderdeel en weging
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Details
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- You must have a minimum of 5.0 in your homework and a minimum of 5.0 in your combined exams to pass the course
- All homeworks count towards the final mark.
- There is a penalty of 25% per day for late hand-ins, with a maximum cut-off of 2 days. This penalty may be waived in case of sickness, but we may need proof from the studieadviseur.
- Exams are closed book and will be written (in-person), assuming no pandemic-related developments.
- In case a student decide to make the Hertentamen, the Hertentamen will count for 50%. It still holds that a student must have a minimum of 5.0 for the Hertentamen and a minimum on 5.0 for the average of the homework.
Inzage toetsing
- Deeltoets marks will be released on Ans
- After the release of marks on Ans, students have 1 week to ask questions regarding their marks
Opdrachten
- In-class assignments are graded pass/fail and individual
- Homework assignments are graded and individual
- Individual homework grades will be released on Canvas, along with written feedback
Fraude en plagiaat
Dit vak hanteert de algemene 'Fraude- en plagiaatregeling' van de UvA. Hier wordt nauwkeurig op gecontroleerd. Bij verdenking van fraude of plagiaat wordt de examencommissie van de opleiding ingeschakeld. Zie de Fraude- en plagiaatregeling van de UvA: http://student.uva.nl
Weekplanning
Homework must be handed in before 23:59 the Sunday after it is set. PDFs only---handwritten answers are fine, but must be scanned in and legible. In-Class exercises are submitted via GradeScope.
Rooster
Het rooster van dit vak is in te zien op DataNose.
This course will be delivered in English.
Verwerking feedback studenten
Hieronder vind je de aanpassingen in de opzet van het vak naar aanleiding van de vakevaluaties.
Coördinator