This is the webpage for the Honours/MSc course Simulating Language, running in 2014/2015. We will add links to materials (lecture slides, readings, lab exercises, code) to this page – you should only need to use Learn for electronic submission of your assessed work.
Your friendly and dedicated teaching team
Kenny Smith (that’s me) is the course organiser and main lecturer. If you need to speak to someone about something, I should be your first port of call. Best way is to just grab me after class, or email me.
Jon and Marieke are are our lab demonstrators and are happy to receive emails with questions about the lab materials.
Jenny Culbertson will be doing a little guest teaching in week 8.
Class times and venues
All the information is in the week-by-week timetable – this version accurate as of 4/3/15. Class meets Monday, Thursday and Friday. We have a mix of lectures (held in the Gaddum Lecture Theatre, 1 George Square) and lab classes (held in room 1.16, Dugald Stewart Building).
Lectures are always 2.10pm-3pm. Labs will take place at 2pm, 3pm and 4pm – you only need to attend one of these times, and it needs to be the same timeslot every time. Please follow this registrations link to indicate your time preference: REGISTER. N.B. Please don’t indicate 2pm unless you really can’t make any of the other times! For some students the 2pm slot will be the only one they can fit in their timetable.
Enthought Python
All the lab classes involve working with simple computer programs: we give you some computer code, and a worksheet explaining it, and ask you to work through some questions using the code. We don’t assume any previous experience with computer programming, but we do expect you to try. Every year the majority of the class arrive having never programmed before and end the semester being able to adjust and run interesting simulation models.
We recommend that you do your best to tackle each lab worksheet before the lab class – that way you can see what we are trying to get you to do, have a go yourself, get stuck at some point, then come along to the lab where we can work through it with you. This will be quite intimidating if you have never programmed before, but stick with it!
You can use the computers in the lab teaching room (DSB 1.16) – we will add your University card to the access list. Or (or as well) you can install a version of python (the programming language we will be using) on your laptop or home machine. I recommend Enthought Canopy, which installs python and a whole bunch of useful libraries, and is the programming environment we will be using in labs. You can download it here – note that on the bottom right there are helpful links that show you exactly how to install it.
Other support for programming
Your first port of call if you want help with programming should definitely be the teaching team for this course, and the best time to get us is during the labs. But if you want a different perspective, or you want to do some additional general programming practice and would like a steer, check out the PPLS Software Development Open Learning page, and/or get in touch with Alisdair Tullo (contact details on that page), who is our in-house software development support.
Assessment
Assessment will be in the form of two take-home papers, each of which will consist of a mix of short-answer and more demanding questions. All of the assessments involve using the code we work with in lab classes, to answer questions relating to the topics covered in the lectures and labs. Together, they will cover everything in the course. You will be given at least a week to complete the assignments (see the week-by-week timetable for the relevant dates).
For undergraduates, the two assessments will have equal weighting in your final mark. For postgraduates, the second assessment is worth 70% of the total mark.
Assessment 1 is now available! Due date: 12 noon, 2nd March.
Undergraduates: this assessment is worth 50% of your course mark. Postgraduates: this assessment is worth 30% of your course mark.
We are doing electronic-only submission – please submit via TurnItIn, there is no need to hand in an accompanying hard copy.
There is an FAQ for Assessment 1 (or in fact just a list of the questions I have been asked about it, even once).
Assessment 2 is now available! Due date: 12 noon, 13th April.
Undergraduates: this assessment is worth 50% of your course mark. Postgraduates: this assessment is worth 70% of your course mark.
We are doing electronic-only submission – please submit via TurnItIn, there is no need to hand in an accompanying hard copy.
There is an FAQ for Assessment 2.
Course materials
We expect you to do the readings before the associated lectures – we will assume you have done in class, and you’ll have to talk about them.
Week 1
- Lecture 1
- Lecture 1 pre-reading
- Lecture 1 slides
- Optional additional reading post lecture 1 (on the role of models in evolutionary biology)
- Lab 1
- Lab 1 worksheet
- Example functions for section 8 of worksheet 1 (don’t look at these until you have tried yourself!)
- Lecture 2
Week 2
- Lab 2
- Lab 2 worksheet
- Worksheet 2 Code Walkthrough
- Code for lab 2: signalling1.py
- Lecture 3
Week 3
- Lab 3
- Lab 3 worksheet
- Code for lab 3: signalling2.py
- Lab 4
- 2015_worksheet04
- Code for lab 4: evolution1.py
- Lecture 4
Week 4
- Lecture 5
- Lab 5
- Lab 5 worksheet
- Code for lab 5: learning1.py
- Additional code: run_example.py
- Additional notes/advice on plotting and producing better graphs
Week 5
- Lecture 6
- Lab 6
- Lab 6 worksheet
- Code for lab 6: learning2.py
- Lecture 7
Week 6
- Lab 7
- Lab 7 worksheet
- Code for lab 7: learning3.py
- Notes from Lab 7, by Jon
- Lecture 8
- There is no pre-reading for Lecture 8 – get started on the pre-reading for lectures 9 and 10!
- Lecture 8 slides
Week 7
- Lecture 9
- Lecture 10
- Lab 8
- Lab 8 worksheet
- Code for lab 8: bayes1.py
- Bonus code for lab 8, showing how to plot histograms: bayes_histograms.py
- Notes from Lab 8, by Marieke
Week 8
- Lecture 11
- Lab 9
- Lab 9 worksheet
- Code for lab 9: U18_bayes.py
Week 9
- Lecture 12
Week 10
- Lecture 13
- Lab 10
- Lab 10 worksheet
- Code for lab 9: bayes2.py
Week 11
- Feedback lecture
- Lecture 14
- Lecture 14 slides
- Optional reading for Lecture 14: Smith & Kirby (2008)
- Lecture 15
- Lecture 15 slides
- Optional reading for Lecture 15: Kirby, Cornish & Smith (2008)
