Computer Science 470 and 570 at Northern Arizona University, Spring 2023

Topic: Artificial Intelligence.

Dates: 18 Jan 2023 to 12 May 2023

Meeting time/place: MoWeFr 9:10AM - 10:00AM, Eng 120.

Course materials adapted from Dr. D’s Spring 2018 offering and my previous offering.

Syllabus: Google Doc.

Weekly schedule of homeworks

Each homework must be submitted on bblearn as a single PDF report, with all of the code, results, and answers to questions.

Please make sure that your report contains the commands and output as shown in this example output file. For each test command

• There should be a >>> prompt followed by the test command,
• followed by the output of that command (from print statements and/or the return value),
• followed by a newline (to provide visual separation between each command). If you do sys.ps1=’\n>>> ’ then that will print a newline before every command prompt.

One way to do this is by simply running “python” which starts the interactive REPL, and then paste your code in (make sure your code does not have any empty lines in the middle of function/class definitions, and that you do have an empty line at the end of each function/class definition). A more automated way to do this (less tedious copy-pasting) is by running your python script through interpreter.py, for example:

$ python interpreter.py example_homework_code.py 


>>> def add(x, y):
...     result = x + y
...     return result
... 

>>> # if you want to use interpreter.py, then only put empty line at the

>>> # end of function/class definitions, as above (not inside of

>>> # function/class definitions).

>>> add(1, 2)
3

>>> add(3, 10)
13

Each homework assignment is due on the Friday of the corresponding week, at 11:59PM. Homeworks will require use of python (I recommend anaconda), I recommend using an IDE such as emacs in which you can see the code, send lines interactively to the python interpreter with a keystroke (control-enter in emacs), and immediately see the results/output after running each line of code.

• Jan 19: Homework week 1: getting started with python.
• Jan 26: Homework week 2: Boggle part 0
  • Boggle wikipedia page.
  • Chapter 1 slides.
• Feb 2: Homework week 3: Boggle part 1
  • Chapter 2 slides.
• Feb 9: Homework week 4: Boggle part 2
  • Chapter 3.1 uninformed search slides.
  • Friday Feb 10: bring your boggle program to class to compete for EXTRA CREDIT: fastest program that gets the right answer wins! (no false positive words, no false negative words) put your program in this google drive folder.
• Feb 16: Homework week 5: Route-finding part 0.
  • Chapter 3.2 informed search slides.
• Feb 23: Homework week 6: Route-finding part 1.
• Mar 2: Homework week 7: Route-finding part 2.
  • Fri Mar 3: no class, but please start working on practice mid-term solutions.
• Mar 9: week 8, no homework, Mid-term review and exam.
  • Mon Mar 6: review session: present your practice mid-term solutions in class for extra credit on the actual mid-term exam.
  • Weds Mar 8: mid-term exam.
  • Fri Mar 10: no class.
• Mar 16: spring break!
• Mar 23: Homework week 9: Halma part 0.
  • Mon Mar 20: route-finding extra credit tournament. fastest program that gets the right answer wins! Bring your program on a laptop /usb drive or put it in this google drive folder.
• Mar 30: Homework week 10: Halma part 1.
  • Slides ch5.
• Apr 6: Homework week 11: Halma part 2.
• Apr 13: Homework week 12: Image classification part 0: installation and visualizing images.
  • installation instructions.
  • intro to machine learning slides.
  • slides about learning a linear model using torch.
  • Mon Apr 10: extra credit Halma tournament! Bring a laptop with your code to class to compete for extra credit points.
  • Fri Apr 14: class cancelled, come to ASU West Machine Learning Day.
• Apr 20: Homework week 13: Image classification part 1.
  • Weds Apr 19: office hours cancelled, instead come to my talk “Intro to Deep Learning in R” for ResBaz AZ 2023, 10:15-noon DuBois Juniper, extra credit homework score.
• Apr 27: Homework week 14: Image classification part 2.
  • slides with test accuracy figures.
• May 4: reading week, exam review (questions 4-10) Weds May 3, present your solution for 10 extra credit points toward your exam score.
• May 11: finals week, exam Weds May 10, 8-9:30.

Homeworks will be graded using this General Usage Rubric.

Video Resources

• Google Drive folder with videos from my Spring 2021 class.

Textbook

The optional readings will be from Artificial Intelligence: A Modern Approach by Russell and Norvig.

• Dead tree book at Cline (on 4 hour reserve).
• Amazon

Exercises

AIMA Exercises

Software

pytorch

Tutorial explaining pytorch installation under anaconda.

The command I used to install was:

conda install pytorch torchvision cpuonly -c pytorch

After that you should be able to do import torch in python.