Check this page regularly, for the overview of subjects may be updated during the course.
Week 1 (36)
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Wed, Sept 4
NO CLASS: Master introduction
Fri, Sept 6
Double slot (two lectures); make sure to attend! Next week, you will have to work on your own, so make sure you know what is expected, otherwise you will already fall behind.
Subjects: Course introduction, Probability theory,Independence relations and Graphical models
Literature (from Syllabus): Ch. 1, Ch. 2, Ch. 3: § 3.1, [3.2.1]
Exercises (from Syllabus): 1.1, 1.2, 2.1 - 2.6, 3.1, [3.2, 3.3], 3.4
Assignments (on Blackboard): Already start working on the Practical assignments! (Can't find a partner? Try Blackboard's discussion forum.)
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Week 2 (37)
NO CLASS:
SELF-STUDY(!!)
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Mon, Sept 10 -- Fri, Sept 13
Based upon the Sept. 6 classes, you can (and should!) spend this week on the following:
- Review the literature from the Sept. 6 classes, possibly with the help of these videos: 🎦 Ch2, 🎦 Ch3.1
- Do the exercises from the Sept. 6 class (Starred (*) exercises come with answers or solutions (in Syllabus Chapter 8)). Need help?: contact your fellow students on the Blackboard discussion forum or ask your lecturer at the next class.
- Find an assignment partner and enroll in a group on Blackboard
- Work on Assignment A: A1 - A5; prepare A11, A12
- Ch. 3: § [3.2.1] is optional material that may be helpful to read or 🎦 watch prior to next week's class.
- Have you already started reading the literature for next week? Then with Ch. 3: § 3.2.2 you're all set to also do Assignment A, A6 - A10 and complete the assignment.
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Week 3 (38)
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Wed, Sept 18
Double slot (two lectures)! If you have questions about Ch.2, Ch. 3: § 3.1 or Assignment A please ask them during today's classes.
Preparation: read literature in advance
Subjects: Graphical models, Introduction to inference
Literature: Ch. 3: § 3.2.2, 3.2.3, Ch. 4: § 4.1
Exercises: 3.7 - 3.14, 4.1, 4.2, 4.8
Assignments: wrap up Assignment A and start on B (all except B4 can already be answered)
Fri, Sept 20
Preparation: read Ch. § 4.2.1 including notation block
Subject: Inference in singly connected graphs (SCGs) and trees
Literature: Ch. 4: § 4.2.1, § 4.2.2
Exercises: 4.3 - 4.7
Directly after class: 🎦review proofs for computation rules and try Exercises 4.4a and 4.5a. It is really important to train yourself with these exercises!
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Week 4 (39)
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Wed, Sept 25
Deadline Assignment A (directly after class)
Preparation: read § 4.2.3
Subject: Examples in SCGs; intro to inference in multiply connected graphs (MCGs)
Literature: Ch. 4: § 4.2.1, § 4.2.2, § 4.2.3
Exercises: 4.9, 4.10, 4.13
Afterwards: really train yourself with the exercises!
[Optional: additional slides for those interested in Junction Tree propagation]
Fri, Sept 27
Preparation: read § 4.2.3
Subject: Loop cutsets
Literature: Ch. 4: § 4.2.3
Exercises: 4.11, 4.12, 4.14
Assignments: you have now seen enough to complete Assignment B
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Week 5 (40)
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Wed, Oct 2
Preparation: read literature below in advance. To understand the relation between BNs and the Bayesian approach used in the PP part, also consider these slides.
Subject: Probabilistic programming, part I (Bayesian data analysis and continuous models)
Literature: BDA3-Ch1 up to and including § 1.7
Assignments: start preparing Assignment C; we assume that you are able to set up all software on your favorite OS
Fri, Oct 4
Deadline Assignment B (directly after class)
Preparation: read literature in advance
Subject: Probabilistic programming, part II (inference in continuous models)
Literature: RR04 up to and including § 3.2 (skim the rest)
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Week 6 (41)
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Wed, Oct 9
Preparation: have Stan up and running to benefit most from today (also see preparation for Friday)
Subject: Probabilistic programming, part III (Workflow and practical session)
Literature: Same as above
Fri, Oct 11
Double slot! First a lecture, then a Stan practical
Preparation for lecture: read §5.1, §5.2.1
Subject: Graph construction
Literature: Ch. 5: §5.1, §5.2.1 and § 5.2.2
Exercises: 5.1-5.3, 5.4a
Preparation for Stan practical: Make sure you have all software necessary for Assignment C up and running, otherwise this session will be rather useless!
- See the Stan documentation
- Getting started with RStan; for this you will need to install RStan
- If you like to use Stan from Python, try CmdStanPy
- 🎦watch the following videos from the playlist of this Stan tutorial: 1 or 2 (depending on your choice for R or Python), and 3--7
- Note that Stan supports every C++ compiler except for the Windows MSVC one (which doesn't adhere to conventions). Windows users therefore need to use a different C++ compiler such as mingw. Alternatively: use Linux or OSX.
Subject: Practical session probabilistic programming for Assignment C
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Week 7 (42)
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Wed, Oct 16
Preparation: read the literature
Subject: construction cntd
Literature: Ch. 5: § 5.2.2 and § 5.3.1
Exercises: 5.4b, 5.7, 5.8
Fri, Oct 18
Preparation:read the literature
Subject: Probability elicitation; Noisy-or gate
Literature: Ch. 5: § 5.3.1, 5.3.2, 5.3.3
Exercises: 5.4cd, 5.9, 5.11
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Week 8 (43) |
Wed, Oct 23
Double slot, starting at 1pm! First coaching, then a lecture.
Preparation for coaching/ Q&A: prepare all your remaining questions about assignment C
Preparation for lecture: read the literature
Subject: Probability elicitation methods, Sensitivity Analysis
Literature: Ch. 5: § 5.3.3 and § 5.3.4, Ch. 6: § 6.1.1
Exercises: 5.5, 5.6, 5.10, 6.1a-d, 6.2ab, 6.7
Fri, Oct 25
Preparation: read Ch. 6: § 6.1.2
Subject: Sensitivity Analysis cntd
Literature: Ch. 6: 6.1.2, § 6.1.3
Exercises: 6.1ef, 6.2cde, 6.3-6.6
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