University of Minnesota
CSci 5512: Artificial Intelligence II
CSci 5512 Syllabus

CSci 5512

Artificial Intelligence II

Spring 2019

Meeting time and place: Lecture (002): 11:15 A.M. - 12:30 P.M. Monday/Wednesday Mechanical Engineering 108.


Name James Parker
Email jparker (at) cs (dot) umn (dot) edu
Office Shepherd Laboratories 391
Office Hours Please see "Office Hours" on the class homepage (below).
Class homepage

TAs: The TAs are primarily in charge of grading and office hours. Their names, contact information, and office hours are posted in the "Office Hours" page on the class homepage.

Text: Russel and Norvig, Artificial Intelligence A Modern Approach Pearson, 3rd edition. There may also be other resources linked to the class web page.

Class Website: There are two websites associated with this course: canvas and a public cse website. Canvas is primarily used to submit homework, see grades, talk on a forum and see announcements. The public cse webpage will have the schedule and any resources used in lecture. Please check the schedule frequently for any changes due to the pacing of the class. Important announcements will also be sent out through email (Labeled with "[CSci5512]" in the title).

Prerequisites: Before taking this course, you should have:

  • Basic knowledge of computer programming.
  • Knowledge of common data structures (graphs and trees).
  • Solid understanding of statistics (probabilities and random variables).
  • Solid understanding of mathematics (calculus, especially derivatives).

General Course Description: This course covers Artificial Intelligence (AI) that deals with uncertainty or learning. We will cover: a brief review of uncertainty and probability, Bayesian networks, Markov models, decision trees/networks, neural networks and reinforcement learning.


For all graded work, please address any concerns within two weeks of receiving the grade. We will not change grades after two weeks. Here is the amount each of the items will contribute to your overall grade:

Homework (5 of them)                              50%
Midterm 1 (Wednesday, March 6)                    15%
Midterm 2 (Wednesday, April 17)                   15%
Project/Final Exam (Tuesday, May 14, 1:30-3:30pm) 20%

Course Content and Components:

  • Readings/Lecture: Approximately 30 pages from the textbook or other papers.
  • Homework: There will be 5 homework assignments (10% of the grade each). These may include some (pyton) programming. Late submissions are reduced by 15% for every day late (no credit after a week).
  • Project: Each student has the option to either take a final exam or complete a project. The project should involve one of the following: (1) conduct an experiment, (2) do a literature review or (3) prove theoretical results. I encourage you to pick a topic that is personally interesting to you. Artificial intelligence is a broad field and you can frame the topic of your choice in this way. If you wish to do the project in a group, you must first receive the instructor's consent.
  • Examinations: There will be two midterm exams (15% of the grade each), and a final exam (20% of the grade, if you choose not to do the project). Dates for these are listed on the schedule. Exams are open book and notes.

Grading for this course is on an absolute scale, so that the performance of others in the class will not negatively affect your grade. Final grades will be assigned based the following scale:

      93.0% -- 100.0%   A
      90.0% --  93.0%   A-
      87.0% --  90.0%   B+
      83.0% --  87.0%   B
      80.0% --  83.0%   B-
      77.0% --  80.0%   C+
      73.0% --  77.0%   C
      70.0% --  73.0%   C-
      67.0% --  70.0%   D+
      60.0% --  67.0%   D
       0%   --  60.0%   F
For S/N grading, a satisfactory grade (S) requires a weighted score of 70 or above.

Scholastic conduct: Both homework and written assignments are individual assessments (unless explicitly stated otherwise). This means your discussions about the questions can only cover the general approaches necessary to solve the problem. You should never share your work or see another student's answer to any part of the problem. It is also not allowed to work so closely together that your answers appear very similar, even if nothing was ever explicitly shared. If you have any questions about what is and is not allowable in this class, please ask the course instructor.

Disability Accommodations: We desire to make learning rewarding and fun for all students and make every attempt to accommodate anyone who has a desire to learn. If you require special classroom or test-taking accommodations, you need to contact the University Disability Services and also notify the instructor as soon as possible at the start of the semester (no later than 3 weeks prior to the first examination).