This course delves into the intersection of cognitive science and AI, starting with the exploration of individualized AI models to simulate unique cognitive processes. Students will begin by building personalized AI systems, gaining hands-on experience with AI technologies tailored to cognitive models. The course then transitions to foundational AI algorithms and neurobiological basics, helping students understand core principles of both artificial and human intelligence. Topics include cognitive function modeling, ethical implications, and applications across various fields. This course equips students with deep insights into the synergy between AI and human cognition, fostering skills applicable in technology, neuroscience, and beyond.
Note: This module will fulfill CHS Artificial Intelligence Common curriculum or CHS Scientific Inquiry (but not both). However, this module will not fulfill CDE Artificial Intelligence Common curriculum.
Student Reviews
Anonymous AY25/26 Sem 2
About the Instructor
My professor always encouraged questions from the class, which deepened my understanding of the computing and psychological concepts taught. He explained key concepts from the material using engaging and conversational language, which made it less complicated to grasp. I like that my professor regularly solicited feedback via Google Forms in class, and even conducted in-class quizzes and debates to truly hear from his students.
Content (Structure/Organization)
The course-wide curriculum was structured around 2 major components: cognitive science and computer science. Foundational concepts for both components were fortified every lesson, and I felt confident enough to link them in reports and class presentations by the halfway mark of the semester. I found it challenging to complete in-class quizzes, which lasted 10 minutes but tested a lot of psychology-related material on top of my ability to formulate an open-ended response.
Manageability of Workload
I spent around 4 hours per week reading through the notes before class and posing clarifying questions to my professors before lessons. For assignments, I had to spend 2 days on each technical assignment involving coding (there were 2 such assignments) making sure my code ran and produced readable output. This was challenging and taxing because the expectation was to exercise critical thinking by thinking about how we got to the answers, instead of getting AI to write these explanations.
Ease/Difficulty of Attaining Grades
The professor is generally harsher in handing out good grades, as he mentioned at the start of the semester. What it takes to stand out is regular class participation, an engaging individual oral presentation, and an interdisciplinary mindset that can articulate the connections between cognitive science and computer science.
Learning Value/Recommendation
8/10. I found it really valuable that I could work on a computing project that builds my portfolio, even as a non-computing student. I gained so much insight into agentic AI, software engineering practices, and how AI is structurally similar and different to the human mind. I can better appreciate the multifold considerations involved in building apps and systems that hold user attention and address their fundamental needs.
Anonymous AY24/25 Sem 2
About the Instructor
Prof Filippov is very passionate and knowledgeable on the topics covered. Theres always value added when we ask questions and he responds enthusiastically without criticism.
Content (Structure/Organization)
The course was organised with various topics relating to cognitive neuroscience and artificial intelligence, varying from scientific breakdowns of the brain to how Netflix is being designed.
This course definitely has some structure to it, especially as assignments are based on previous seminars covered.
Learning outcomes were clearly defined at the start of the semester, with most of them being fulfilled.
In terms of flexibility, we were allowed to present on any topics relating to the course, where our class was quite diverse in selecting their topics, ranging from AI in esports to fintech. We definitely learned more from the presentations by our peers
Manageability of Workload
On the lower end, 1hr a week to refresh content if [you] need to. On the higher end for assignments, generally take longer depending on [your] familiarity with programming
Ease/Difficulty of Attaining Grades
A is achievable given a technical background. Its quite a hurdle if you are unfamiliar with coding or using google notebooks. Prof is quite liberal with the grades.
Learning Value/Recommendation
Biased perspective, coming from a technical background and familiarity with AI, I would say its okay.
However, for a non STEM student, I think this course can be very useful to learn more about AI’s deep relation and history with the mind.
Additional Comments/Word of Advice
The projects are really fun!