IT3105 Web Page - Autumn 2016
This course teaches Artificial Intelligence programming via
several medium-sized AI projects involving concepts and methods such as best-first search, minimax with alpha-beta pruning, constraint reasoning, propositional logic, first-order predicate logic, decision-tree learning, evolutionary algorithms, neural networks, Bayesian classifiers, boosting, bagging and particle swarm optimization...to name just a few. The exact topics can vary from year to year.
There will be no tight connection to a single
programming language, and there will not be a lot of lecture time
devoted to language learning. We will DIVE RIGHT IN to sizeable
projects, some of which may have a recommended language
but normally no strict requirement. Students are normally free to use
the language(s) of their choice. However, it is recommended that they choose
a language that supports the formation and manipulation of large sequences (often nested) of symbols (i.e. mixtures of letters and numbers) such as "Age(Fred) + 18 = 35 AND Height(Wilma) > 175".
When instructors provide supporting code, it will normally be in a standard
language such as Python, JAVA, C++ or MATLAB. No machines maintained by the department will house any of the software needed for this course. Students are expected to download all relevant software (all of which is free and easily accessible) to their own machines.
The course lab hours are not used in the traditional sense (of all students meeting in a room and doing "lab exercises"). The lab hours are simply the times at which our student assistant(s) are available to help students. In addition, in the rare instance that an instructor feels the need for extra lectures, (s)he may choose to use the course lab hours.
This course is definitely NOT one that a student can expect to join late in the semester or ease into
. Work on the first project should begin
immediately after the first lecture. Each project and module involves considerable programming effort, so you will need to hit the ground running
at the beginning of each one. Waiting until the last minute (weekend) has been the demise of many students in this course.
This year (2016), It's Learning will be the course's main source of information.
It is very important that you read the course grading policy.
Lecturer: Kazi Shah Nawaz Ripon (ksripon<at>idi.ntnu.no)
Course Coordinator: Jo Skjermo (jo.skjermo<at>idi.ntnu.no)
Course & Student Assistants:
- Audun Liberg (audunlib<at>stud.ntnu.no)
- Kari Skjold (karifs<at>stud.ntnu.no)
- Johannes Bredholt Moskvil (johannbm<at>stud.ntnu.no)
- Torbjørn Vallestad (torbval<at>gmail.com)
Help hours in room 424 (Vembi), Høgskoleringen 3 (P15)
- Tuesdays: 14:15 - 16:00
- Fridays: 10:15 - 12:00
To contact course instructors and assistants, use their individual email addresses, not It's Learning.
- Lectures will take place in Room S1 of Sentralbygg 1 (Stripa)
NTNU's official web page for this class is here
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