TDDC17 Artificial Intelligence
Due to current COVID-19 restrictions most of the TDDC17 lab supervision will be done remotely using Microsoft Teams software. This means scheduled lab sessions at the campus (lab rooms) will be unmanned and you are welcome to use the computer resources (see the section below "Working on Lab Assignments"). Access to assistants for answering questions/making demonstrations will take place remotely through Microsoft Teams.
Registration and Microsoft Teams
Registration for the labs should be done using the Web-Reg on-line registration system.
Due to the high number of students participating in the course this year single-person lab groups are not allowed - i.e. each group must have two students solving the labs together.
To register for the labs and for information about how the WebReg System works go to:
Note: Grupp 6 and Grupp 7 is reserved for U3 students only.
If you require assistance during a lab session simply indicate in the "General" chat that you need help or want to make a demonstration of a lab assignment and specify the sub-group number. In case there are multiple requests posted, the lab assistant will process the requests in order of posting. Your specific questions will then be handled in your private sub-group chat. This will be done by either a text response from your assistant or in case of more complex questions/lab assignment demonstrations, a video/audio/screenshare call will be arranged.
Working on Lab AssignmentsMost of the lab assignments can be done remotely and require only basic setup that includes downloading free software i.e. java and/or python and Eclipse/PyCharm IDE. The exception is part of the Planning lab (lab 4) which requires running specialised planners that are installed on the computers available at LiU. In this case one is required to remotely login to perform this part of the assignments.
There are three ways you can work on your lab assignments this year and we strongly encourage you to work on the labs in the distance mode:
- At a distance using your own computer. Simply follow the instructions on the specific lab assignment's web-page to download the necessary code and software.
- At a distance using remote desktop/login with 3 variations. Detailed information can be found here:
- RDP client: supports graphics and the connection is made directly to one of the physical computers available during scheduled lab sessions. This option should ensure best performance.
- Thinlinc client: also supports graphics, but the performance may be lower at times due to hardware limitations and depending on the number of active connections.
- SSH: limited to terminal/console mode. Most limited, but can be used when working on the Planning lab.
- On site (i.e. at campus), during scheduled lab sessions. Note, that restrictions may apply due to current LiU COVID-19 policy.
DeadlinesThere is a semi-hard deadline of having the labs done before the exam. This is beneficial to you in two respects. Firstly, it will help you in passing the exam. Secondly, our ability to correct labs after the period ends decreases radically due to time constraints. For prompt registration of lab results it is best to get them in before the exam.
To make it easier for you to plan your participation in the lab course we have provided you with some guidelines below for when the labs should be completed. These are only guidelines! Remember that there are six labs and roughly twelve teacher aided lab occasions. This amounts to two lab occasions per lab with some slack. We expect lab 1 to take the least amount of time for most while lab 5 may take a bit longer than the rest. Lab 6 is more of a tutorial on Deep Learning. Remember to read the designated chapters in the book in advance to be as efficient as possible. We also encourage you to work outside of the teacher assisted lab hours if possible.
Six labs are planned for the course. Lab specifications will be updated and placed online incrementally during the course. The first lab will be accessible in late august. Note, the main programming language for the labs is Java, with exception of lab 1 which can alternatively be completed in Python. This pertains to lab 1, 2 and 5 in particular.
Lab 1: Intelligent Agents (java/python) , recommended turn-in date: week 36
Lab 2: Search (java), recommended turn-in date: week 38
Lab 3: Bayesian Networks, recommended turn-in date: week 39
Lab 4: Planning, recommended turn-in date: week 40
Lab 5: Reinforcement Learning (java), recommended turn-in date: week 41
Lab 6: Deep Learning, recommended turn-in date: week 41
How to report your resultsThe requirements for reporting your results for each lab assignment are listed on the respective web pages. In some cases, a short demonstration to a lab assistant is required that will be done using a video/audio/screenshare call which you have to request using Microsoft Teams, as explained above. After the demonstration has been accepted, submit the lab in a designated private sub-channel for your group in Microsoft Teams, i.e. using "file/upload" functionality.
Page responsible: Patrick Doherty
Last updated: 2021-09-29