Course Syllabus

Stat 111: Artificial Intelligence Unplugged

Course Information (Spring 2027)

Course Schedule	MWF Spark 335 3:10-4:00pm
Instructor	Nairanjana (Jan) Dasgupta
Office	Neill 115
Weekly Office Hours	W, Th 1-2 (in Neill 115), F 9:30-11:30 (via zoom)
Phone	509-335-3736
Email Address	dasgupta@wsu.edu
Course Website	canvas.wsu.edu
Credits	3
Pre-requisites	None

 

Course Description: What if you could understand—and figure out the tools to build—the technology that is changing the world? In this course, we will explore how AI works: how it learns, recognizes patterns, makes predictions, creates images and text, recommends what you watch and listen to, and powers technologies you use every day. You'll get hands-on experience with using AI in different situations, explore exciting technologies like ChatGPT and discover the very basic mathematics, statistics and computing behind them. We'll also ask the questions that matter: How to use AI effectively? Can AI be trusted? Can it be biased? What is hallucination? How do we know when the answers are likely to be wrong?

No advanced mathematics, statistics or programming experience required—just curiosity about how AI works and where it's taking us. Students from all majors and backgrounds are warmly welcome.

Inclusive Learning Environment: This course is designed to be welcoming to everyone. Students with a wide range of backgrounds — including those new to computing, mathematics, or data — will find that the material builds up from the ground. If you ever feel confused, you are almost certainly not alone. Please speak up in class or reach out privately — your questions make the class better for everyone.

Recommended Text: David L. Poole and Alan K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, 3rd ed., Cambridge University Press, 2023.

Additional reading material will be distributed in class during the semester.  

Required Materials: You will need to have a laptop or web-capable tablet that you can bring with you to each class session and lab session. If you do not have a personal laptop or tablet that you are able to use for class, see the Resources portion of this syllabus for information about the university’s technology rental program.

Course Expectations:

1. Seek Help and Ask Questions: Learning is a collaborative process. Attend office hours, send emails, and ask questions during class. Curiosity and questions are signs of engagement and growth.
2. Be Present and Engaged: Attend class regularly, participate in discussions and activities, and contribute to a learning environment where everyone can succeed.
3. Respect Others and Their Ideas: Statistics and AI often involve multiple perspectives and interpretations. Approach discussions with curiosity, support your viewpoints with evidence, and respectfully consider the perspectives of others.
4. Take Responsibility for Your Learning: Come prepared, complete assigned work, and stay current with course materials. Consistent effort is essential for success in statistics and AI.
5. Use AI and Technology Ethically: Use course technologies responsibly and honestly. AI tools should support your learning and critical thinking, not replace them.

Student Learning Outcomes:

At the end of this course, students should be able to:	Timing	Evaluation of Outcome
Explain how modern AI systems learn from data, make predictions, and recognize patterns.	Weeks 1 - 3	Homework assignments, midterm exam, and independent projects
Describe and apply fundamental mathematical and statistical concepts used in AI systems	Throughout course, with more focus in Week 3 - 8	Homework assignments, midterm exam, and written projects
Evaluate the accuracy, uncertainty, bias, and limitations of AI systems.	Weeks 9 - 15	Homework assignments, written projects, midterm exam, and final exam
Apply AI and statistical methods to real-world problems and critically evaluate their effectiveness	Throughout course	Independent project and final exam.

Expectations for Student Effort: STAT 111 is a 3-credit course. The 3-credit designation normally implies that on average the student is expected to spend 2hrs (per lecture) = 6 hours per week on average outside class working on this course.

Assignments

Homework: Homework assignments will generally be due each Wednesday. A two-day grace period is available, and assignments submitted by Friday will not be considered late. If you experience unexpected hardships or emergency, please contact the instructor before the deadline whenever possible so that we can discuss appropriate accommodations.

Written Projects: The course projects are designed to provide hands-on experience with data analysis, interpretation, and written communication. Students will complete three written projects during the semester. These projects will require students to apply course concepts to real or realistic datasets and communicate their findings in a clear, professional manner.

 

Exams

This course will consist of two midterm exams (each worth 10% of your final grade) and a comprehensive final exam (worth 15% of your final grade) which will cover material from throughout the entire course.

 

Grades

Assignment	Final Grade Percentage
Class Participation	5%
Homework	30%
Projects	30%
Exam 1	10%
Exam 2	10%
Final Exam (Comprehensive)	15%
Total	100%

Grade Cut-offs:

A (93 - 100%) • A- (90 - 92.9%) • B+ (87 - 89.9%) • B (83 - 86.9%) • B- (80 - 82.9%) • C+ (77 - 79.9%) • C (73 - 76.9%) • C- (70 - 72.9%) • D+ (66 - 69.9%) • D (60 - 65.9%) • F (<60%)

 

Rounding Statement: In case of numeric grade value being between two letter grades, instructor will round up.

 

 

Policies

 

Attendance Policy: Regular attendance and active participation are expected and strongly encouraged. This course relies on in-class activities, discussions, and engagement that support your learning and professional development. If you must miss class for a legitimate reason, please notify the instructor as soon as possible. We understand that occasional absences happen. Communicating with the instructor promptly helps ensure that you stay informed about missed content and expectations.

 

• Attendance and participation account for 5% of your final course grade.
• Active participation is required. Class meetings will include discussions, activities, and collaborative learning opportunities that cannot be fully replicated outside of class.
• Remote attendance is generally not available. If you have a documented need for temporary remote access, please contact the instructor in advance to discuss possible accommodations.
• Course materials and lecture notes will be posted online, but reviewing posted materials is not a substitute for attending class and participating in course activities.

 

Academic Integrity and Responsible Use of Artificial Intelligence: Because Artificial Intelligence is a central topic of this course, students will have opportunities to use AI tools such as ChatGPT and other generative AI systems. The goal is not to prohibit these technologies, but to help students learn to use them responsibly, transparently, and critically.

 

1. Your Work Must Demonstrate Your Learning. Students are expected to complete assignments, examinations, projects, and other assessments in a manner that demonstrates their own understanding of the course material. Submitting AI-generated work as one's own work, without acknowledgment or meaningful contribution by the student, constitutes a violation of academic integrity.
2. Permitted Uses of AI. Unless an assignment states otherwise, students may use AI tools for activities such as:

• Brainstorming ideas or questions
• Explaining a difficult concept
• Generating examples for further investigation
• Getting feedback on writing
• Checking grammar, clarity, or organization
• Exploring alternative approaches to a problem
• Writing or debugging computer code
• Learning how AI systems respond to different prompts

Students remain responsible for the accuracy and quality of anything they submit.

 

3. Restricted or Prohibited Uses. Students may not use generative AI to:

• Complete an examination or quiz
• Generate an answer and submit it as their own without acknowledgment
• Complete an assignment intended to assess individual understanding
• Fabricate data, results, references, or sources
• Misrepresenting AI-generated material as independently produced work
• Circumvent course requirements or assessment procedures

I will have certain assignments designated AI-free, AI-assisted, or AI-required. Students are expected to follow the requirements specified for each assignment.

 

4. Disclosure of AI Use. When AI has made a substantive contribution to an assignment, students should disclose its use. Depending on the assignment, disclosure may include:

 

AI Use Statement: I used [AI tool] to [describe how it was used]. I reviewed and revised the output and am responsible for the final work submitted.

 

5. Students Are Responsible for AI-Generated Information. As we will discover in the class, AI systems can produce incorrect, biased, incomplete, or fabricated information. Students are responsible for verifying AI-generated information before using it in their work. In particular, students should independently verify:

• Mathematical calculations
• Statistical analysis
• References and citations
• Factual claims
• Computer code
• Data and reported results

 

6. AI and Collaboration. Using AI does not eliminate the requirement to follow the course's rules regarding collaboration. Students may not use AI to disguise unauthorized collaboration or to submit another person's work as their own.

 

7. The Principle Behind This Policy. This course is designed to help students become informed users and critical evaluators of Artificial Intelligence. Learning to use AI effectively is part of becoming AI-literate; learning when not to use it is equally important.

 

Students’ Rules of Conduct: Students are expected to adhere to the WSU Honor code rules. Further, students are expected to make every effort to complete all requirements and to contact the course instructor to discuss any concerns. Please see University Syllabus for more information.

 

For other policies please refer to the University Syllabus: https://syllabus.wsu.edu/university-syllabus/

 

Resources

 

Mathematics Learning Center: Successful students make use of available resources, so don't struggle when help is just a few steps away. We want you to succeed, we're here for you, and our tutoring services are completely FREE!

 

The MLC is located in Cleveland Hall 130. Tutoring begins August 19th. We are open Monday through Thursday from 10am-9pm, Friday from 10am-5pm, and Sunday from 4pm-9pm. Zoom tutoring will also be available. Information can be found on our website: https://daesa.wsu.edu/math-learning-center/.

 

Academic Success and Career Center: WSU offers free tutoring through the Academic Success and Career Center (https://ascc.wsu.edu/channels/tutoring/). See website for more information.

 

Technology Rental: Students are expected to have a laptop or tablet that they may use to access course content both during and out of class.  If you do not have a portable, internet-enabled device that you are able to use for course work, the library offers a Chromebook rental program for currently-enrolled WSU students.  If you are interested in renting a laptop for the semester, please submit the request as soon as possible as all devices are rented on a first-come, first-served basis.

 

Disability Accommodations: Reasonable accommodations are available for students with a documented disability. Please contact Student Accommodation and Disability Resources (SADR) (https://accommodations.wsu.edu/) as early as possible so that appropriate arrangements can be established. Once accommodations are approved, please communicate with me promptly so we can discuss how to best support your success in the course. If your accommodation needs change during the semester or if challenges arise, please check in with me so that we can address concerns in a timely manner.

 

Digital Accessibility Statement: Some materials for this course are not yet available in a digitally accessible format. If you require an accommodation to access digital documents due to a disability, please reach out right away to your home campus’s Student Accommodations and Disability Resources. They can assist with resolving access issues, coordinating support, and ensuring your needs are met throughout the course.

 

Students in Crisis – Pullman Resources: If you are struggling or experiencing mental health issues or need have an emergency. The following is a list of resources available to WSU students.  This information will also be available through MyWSU. 

 

• If you or someone you know is in immediate danger, call 911 immediately.
• AWARE Network: aware.wsu.edu
• WSU Counseling and Psychological Services (CAPS):  509-335-4511
• CAPS After-Hours Crisis Support: 509-335-2159
• 988 Suicide & Crisis Lifeline: Call or text 988

• Crisis Text Line: Text HOME to 741741
• WSU Police: 509-335-8548
• Whitcom / Pullman Non-Emergency Dispatch: 509-332-2521
• WSU Compliance and Civil Rights: 509-335-8288
• Alternatives to Violence of the Palouse: 509-332-4357 (509-332-HELP)
• Pullman 24-Hour Crisis Line: 509-334-1133

Subject to Change Statement: This syllabus and course calendar are subject to change in the event of extenuating circumstances.  For more up-to-date information, see Canvas course.

Tentative Course Schedule

 

Projected schedule for topics in the class. The schedule below provides a general roadmap for the semester. Specific topics, assignments, assessments, and pacing may change based on class progress, student interests, emerging developments in AI, and instructional needs. Any significant changes will be communicated in advance through Canvas and in class.

 

Week	Topic
1	What is AI? History and Current AI, from Turing to ChatGPT
2	Using AI: Common AI Methods and Applications
3	Data, Datasets, and Data Dictionaries
4	Probability and Uncertainty: Understanding Chance
	EXAM 1
5	Sources of Bias and why fairness matters
6	Introduction to Machine Learning
7	Classification and Regression Models
8	Neural Networks: Parallels with the Human Brain
9	Transformer Models and the Role of Attention
10	Catch up, Review
	EXAM 2
11	Generative AI: Concepts and Applications
12	Large Language Models (LLMs)
13	Bias and Fairness: Revisited
14	Ethics, Privacy, and Responsible AI
15	Future of AI and student projects
	FINAL EXAM