INFO 287-11 (2-Units)
Seminar in Information Science
AI In The Library
Fall 2020 Syllabus
Canvas Login and Tutorials
Canvas Information: Courses will be available beginning August 19th at 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.
This 2-unit course runs from September 14th - November 8th. It will be available on Canvas on September 14th.
You will be enrolled in the Canvas site automatically.
This course covers what artificial intelligence is and critical analysis of AI systems, in the context of real-world library and archival AI applications. Programming skills not required.
The general plan of assignments is given below. Specifics will be available in Canvas.
All assignments are due at midnight California time on the last day of their week, except for the final project. The final project is due on Wednesday, November 4, at midnight California time, in order to give time for Q&A.
|1||September 14 - 20||Machine Learning Techniques:
Introduction to ML
|Mental model writeup #1
|2||September 21 - 27||Machine Learning Techniques:
The NIU Dime Novels Collection and text mining
|3||September 28 - October 4||Data and its Discontents:
The Charles Teenie Harris Archive and messy data
|Final project proposal
(CLO 1, 2, and/or 3)
|4||October 5 - 11||Data and its Discontents:
Algorithmic bias and what machines learn
|5||October 12 - 18||The Search for Meaning:
HAMLET and neural nets
|6||October 19 - 25||The Search for Meaning:
Interpretability and intent
|Mental model writeup #2
|7||October 26 - November 1||AI and You:
The URI AI lab: AI as service model
|8||November 2 - 10||AI and You:
(CLO 1, 2, and/or 3)
|Mental model writeup #1||5||September 20|
|Final project proposal||5||October 4|
|Data set investigation||20||October 11|
|Mental model writeup #2||20||October 25|
|Final project||25||November 4
Please note this is a Wednesday, unlike the other assignments.
Other Relevant Information:
Discussion is an important element of this course, as it is where we will explore many of the issues raised in the reading. I expect participants to not merely be curious and thoughtful about the material, but also to hold space for their classmates to learn, ask questions, disagree, and admit uncertainty. To that end, the course participation rubric (available in Canvas) will include social rules based on ALA's Statement of Appropriate Conduct and the Recurse Center's Social Rules.
Course Workload Expectations
Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.
Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.
Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.
INFO 200, other prerequisites may be added depending on content.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Understand and explain the basics of AI: both its underlying principles and common machine learning techniques.
- Discuss realistic ways that AI can be a part of library services.
- Critically analyze potential pitfalls of AI systems, including the role of their underlying data sets and their ramifications in society.
Core Competencies (Program Learning Outcomes)
INFO 287 supports the following core competencies:
- H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.
No Textbooks For This Course.
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
|97 to 100||A|
|94 to 96||A minus|
|91 to 93||B plus|
|88 to 90||B|
|85 to 87||B minus|
|82 to 84||C plus|
|79 to 81||C|
|76 to 78||C minus|
|73 to 75||D plus|
|70 to 72||D|
|67 to 69||D minus|
In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:
- C represents Adequate work; a grade of "C" counts for credit for the course;
- B represents Good work; a grade of "B" clearly meets the standards for graduate level work;
For core courses in the MLIS program (not MARA or Informatics) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
- A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.
Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA).
Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. Make sure to visit this page, review and be familiar with these university policies and resources.
In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.
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