Instructors:

• Keith VanderLinden

Materials:

• Artificial Intelligence: A Modern Approach, S. Russell & P. Norvig, 3rd edition, 2010
  • PDF copy — Printed copies of this 3rd edition are hard to find and the 4th edition is not yet available.
  • Udacity Introduction to Artificial Intelligence — On-line version of the course, S. Thrun & P. Norvig.
• Machine Learning Crash Course — An on-line class, Google.
• Deep Learning with Python, F. Chollet, 2017 (see the code samples).
• Tools
  • MatPlotLib
  • Seaborn
  • NumPy
  • Pandas
  • Keras
• cs344-code, course code.

Weekly schedule: We will meet for lectures in the classroom twice a week, generally on Monday and Wednesday, and for programming exercises in the lab once a week, generally on Friday. The course calendar specifies assignment dates; the due dates, by assignment type, are as follows:

• Guides — the beginning of the first class period of the week for that unit (usually Monday at classtime)
• Labs — the end of the next day after the lab (usually Sunday at 11:59pm)
• Homeworks — the end of the day before the following unit’s lab day (usually the following Thursday at 11:59pm)
• Quizzes — in the last half of the class period on Mondays as scheduled (see the course schedule)
• Projects
  • Projects #1 & #2 — the same pattern as for the homework assignments
  • Project #3 Walkthrough — the end of reading recess
  • Project #4 Showcase — the final exam period
  • Project #5 Final Submission — the end of the day of the final showcase.

Lab Facilities: The Linux partition in SB 354 (a.k.a. the “Gold” lab) is configured to support this course. If you'd like to configure your own machine as well, see the first unit guide for instructions.

Grading:  We will compute your final grade as follows:

• 10% Labs
• 30% Homeworks
• 30% Exams
• 30% Project

We encourage you to work in teams of two for the labs and homeworks. You must do your own work on the exams and the project. When you work in teams, have one team member submit the work for the team and include the names of team members in the submission.

Attendance: We don’t grade on attendance. If you must miss a class for any reason, please let us know so that we can set you up with the appropriate material and activities for the session you miss. If you must miss an exam, let us know in advance, if possible, and set a time to take it before the in-class discussion of the results.

Late Work: We will indicate a due date for all assignments. Anything submitted after that date is late, unless you have made prior arrangements with us. For each late assignment, you will be charged 10% for each business-day that you are late (e.g., if an assignment is due on Friday, you may turn it in on Monday at 10% off, Tuesday at 20% off and so on). We will not accept work that is more than one week late unless you talk to us about the circumstances and negotiate a late-submission plan. We will generally grant up to 33% for these negotiated plans. We will also grant you 4 grace days with no penalty, no questions asked. Please note that grace days do not apply to the reading assignments or the final project and that we do not accept or negotiate for late work after the last day of class. If there are exceptional circumstances, please talk to us so we can work something out.

Disabilities: Calvin University is committed to providing access to all students. If you are as student with a documented disability, please notify a disability coordinator in the Center for Student Success (located in Spoelhof University Center 360). If you have an accommodation memo, please come talk to us in the first two weeks of class.

Incompletes: We will give an incomplete grade (I) only in unusual circumstances, and only if those circumstances have been confirmed by the Student Life office.

Academic Integrity in Computing: Code re-use can be a valuable practice in computing, but mindlessly copying code is not an effective way to learn programming and reusing code or algorithms without attribution is plagiarism. If you’ve copied code with attribution, we’ll grade you on the code you wrote not on the code you copied. If we detect copying without attribution, i.e., plagiarism, we’ll be forced to give you a failing grade for the assignment and perhaps for the course.

Academic Integrity: The student-faculty relationship is based on trust and mutual respect, which can be seriously undermined by the suspicion or reality of academic dishonesty. Academic Dishonesty includes, but is not limited to, plagiarism (students plagiarize when they do not credit the sources of their writing - the words, information, ideas, or opinions of others), improper group work, reuse of a paper from another course and/or cheating on a test. Students are encouraged to speak to their faculty member with specific questions related to academic dishonesty. For further clarification or information, please visit the university policy on plagiarism.

Responsible Use of Technology: We expect you to abide by the guidelines expressed in the policies given in your institution’s technology policy (for students on campus, see Calvin’s Technology Policy Documents).

Etiquette: We expect you to treat students and instructors for this with respect by adopting courteous communication practices throughout the course, either on the videos, which may be recorded, or the online forums. No personal attacks, trolling, bad language will be tolerated.