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Artificial Intelligence, 5.0 c

Course code:1DL340, Report code:11010, 33%, DAG, NML, week: 36 - 43 Semester: Autumn 2014

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Information for reserves

If you are on the waiting list, you will be contacted if there is a place available.

Contact information

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Name: IT-kansliet/Student Office
Email: it-kansli@it.uu.se
Telephone: 018-4717604

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To take this course you must have a student account. As an admitted you can activate your student account via www.uu.se/konto.

Course information

Lab assignments

The course includes two lab assignments. Lab assignments should be solved in groups of four or five students.

Lab Assignment 1: Generate A* based search routines that you can use to compete on the 'Delivery Man' game. More information will be coming soon!

Lab Assignment 2: Implement a Hidden Markov Model based routine that you can use to compete on the 'Where's Croc' game.

Assignments handed in before their respective deadlines (see table below) will be marked and returned as soon as possible. All assignments must be passed in order to pass the lab component of the course.

Note that one of the requirements for a top grade in the course as a whole is that the assignments (serious attempts) have been handed in before their respective deadlines. Assignments handed in after the last scheduled day will be accepted, but may not be marked until the end of the semester. Students who fail to hand in the reports before the end of the semester, fail the course.

Assignment Deadline
1 22nd September
2 13th October

Presentation and Exercises

In addition to the lab assignments, the course includes a presentation assignment which should also be done in groups of two students. Groups should select one of the topics below:

  • Investigate the potential application of a particular technique or class of techniques covered in the course.
  • Discuss current state-of-the-art for a particular technique covered in the course. Include a review of current literature.

The course also includes two class exercise sessions. Activities will be presented on the day to be done in groups. Completion is compulsory.


Examination in this course is a sum of three parts; The lab assignments, the presentation and a written exam, in total worth 5 credits.

The presentation and exercises are graded U (fail) or G (pass). The lab assignments are graded U, 3, 4 or 5. Together (indivisibly) the lab projects and presentation are worth 2 credits.

The written exam is graded U, 3, 4 or 5 and is worth 3 credits.

The total grade on a complete course is the average grade of the lab projects (weighted at 25% each) and the exam (weighted at 50%), rounded to the closest integer. In addition you must pass all the labs and, for grade 5 specifically, all deadlines during the course must have been met.

Lectures, Classes and Labs

NB The order may change. Check your official schedule for location and times.

It is expected that you research the topics presented in the lectures further. It is not compulsory that you use the readings specified: There are many fine sources available for free on the internet.

The numbers in the reading column refer to chapters and section in either Russell and Norvig, Artificial intelligence : A Modern Approach, 3rd Ed ('AI') or Haste et al, The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd Edition ('SL'). Note that SL is available online as a pdf for free.

The lecture slides will be posted as pdf documents. If you cannot view these in your browser, try and download them to your local machine and use Adobe Reader.

Date Title Description Readings
09-Sep Lecture 1 Introduction  
10-Sep Lecture 2 Search 1 AI: 3.3, 3.4, 4.1
12-Sep Lecture 3 Search 2 AI: 3.5, 3.6, 5.1-5.3
15-Sep CANCELLED    
17-Sep Lab 1 Assignment 1: Delivery Man  
19-Sep Lecture 4 Natural Computation  
22-Sep Deadline Assignment 1: Delivery Man  
23-Sep Lecture 5 Planning and Scheduling AI: 10.1-10.3, 10.4.4, 11.1
25-Sep Exercise Class 1 Class Exercise: PDDL  
29-Sep Lecture 6 Markov Models AI: 15.3
02-Oct Lab 2 Assignment 2: Where's Croc  
03-Oct Lecture 7 AI in Computer Games  
07-Oct Lecture 8 Encoding Expert Knowledge  
09-Oct Lecture 9 Bayes Nets AI: 14.1-14.5, 15.4-15.5
10-Oct Exercise Class 2 Class Exercise: Using Bayes Nets to Encoding Expert Knowledge  
13-Oct Deadline Assignment 2: Where's Croc  
14-Oct Lecture 10 Natural Language Analysis  

Course description

"What is intelligence"? is a deep philosophical question. The topic of this course is more practical: how can we make computers perform tasks that - up to now - are difficult for computers? Tasks that require an "intelligent" approach, because computing power alone is not enough.
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Department of Information Technology


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Current department messages
To take an exam, you must sign up for it in the Student Portal no later than 12 days before.
2017-12-06 10:23

Its not possible to take an exam without signing-up for it in the student portal no later than 12 days before the exam.

Please note! If you want to take an exam, but are unable to sign up: Please contact the IT Dept Student Office/IT-kansliet no later than 12 days before the exam date: it-kansli@it.uu.se

Best regards,

IT Dept Student Office/IT-kansliet