IT3105 Instructor's Web Page - Spring 2023

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, reinforcement learning, boosting, bagging and particle swarm optimization...to name just a few. The exact topics will 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. Most students choose PYTHON or JAVA.

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.

Three main topics in this (Spring 2023) version of the course will be, Monte Carlo Tree Search, Reinforcement Learning, and Neural Networks. One or more of the these topics will be central in each of the three projects. The first and second projects will be delivered via a live demonstration to a member of the course staff, while the third project is a video to be delivered online.

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.

The instructor of this course may use two different media to inform students: 1) This web page and 2) BLACKBOARD. In general, this web page is used as a repository for course materials and relatively static schedule information, while BLACKBOARD is used as a repository for student deliveries, a sign-up sheet for demo sessions, a medium for dissemination of certain interim grades (such as those for individual projects), and as a bulletin board for important messages (such as lecture cancellations, changes to demo sessions, etc.).

Grading, Delivery and Attendance Policies

It is VERY important that you read both of these items:

• grading policies
• coding policies

Help Sessions (Online)

Check Blackboard for more details such as the online channel used by each student assistant, any temporary changes to their availability, etc.

People

• Lecturer and Coordinator: Keith Downing (keithd@ntnu.no)
• PhD Assistants:
  • Sverre Herland (sverre.herland@ntnu.no)
  • Even Klemsdal (even.klemsdal@ntnu.no)
• Student Assistants:
  • Magnus Strand (magstra@stud.ntnu.no) ***
  • Emil Hjelle Systad (emilwhj@stud.ntnu.no) ***
  • Yrjar Gedde (ybgedde@gmail.com) **
  • Fredrik Holmeide (fredrik.holmeide@ntnu.no) **
  • Jacob Nitter (nitter.jacob@gmail.com) **
  • Andreas Engebretsen (andreas.engebretsen@ntnu.no) *
  • Tony Shusheng Yang (shusheny@stud.ntnu.no) *
  • Aksel Østmoe (akseloestmoe@gmail.com) *
  *** (150 hours), ** (120 hours), * (100 hours)

  To contact any of the people listed above, use their email addresses, not BLACKBOARD (unless otherwise specified by any of these individuals) .

  Lectures

  NOTE **** In Spring Semester 2023, all course lectures will be pre-recorded and available online. If Covid restrictions prevent physical meetings, the official lecture times (listed below) will be used for general question-and-answer sessions (which will not be recorded). However, if physical meetings are allowed, they will be relatively normal lectures including both a) the same material as the pre-recorded lectures, and b) questions and answers.

  • Location: Room R2 in Realfagbygget
  • Lecture Time: Thursdays 14:15 - 16:00
    • First lecture: Thursday, January 12
    • EASTER BREAK: No lectures on March 30th and April 6th
    • Final lecture: Thursday, April 27

    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.

    ** Follow Blackboard for room-change announcements.

    Important Links

    NTNU's official web page for IT-3105.

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