Logistics
Learning Objectives
At the end of the course, the students should be able to
- Identify societal challenges that can potentially be tackled by AI methods, and determine which AI methods can be applied
- Describe the AI methods covered in the course, including the basic concepts, the key algorithms, and the commonly-used implementation of the methods
- Model the societal challenges as mathematical problems that AI techniques can be applied and propose how to adjust and modify the AI techniques to fit the problems
- Describe the evaluation criteria and methodologies of applying AI methods for social good
- Deliver a report on the course project and present the work through an oral presentation
Learning Resources
The online textbook can be found here.
Slides, references, and additional resources will be provided on Canvas.
If you need to review basics of probability, linear algebra, calculus, check your textbook, Coursera courses, Wikipedia pages, or these videos:
Assessments
The final course grade will be calculated using the following categories:
| Assessment | Percentage of Final Grade |
|-------------------------|---------------------------|
| Class Participation | 10% |
| Paper Reading Assignment| 20% |
| Online Homework | 20% |
| Course Project | 50% |
- Class participation. The grading of the class participation will be mostly based on attendance, checked by in-class polls and asking and answering questions in class. Other factors include asking and answering questions on Piazza.
- Paper reading assignment. The course will require all students to complete several paper reading assignments individually. In each assignment, the students are required to provide a summary of the paper/article, questions, and discussion.
- Online homework. The course will require all students to complete several online homework assignments individually. Each assignment will involve checking the understanding of basic concepts and working through the algorithms presented in class on example problems. Most questions are multiple-choice questions or numerical answer questions but the students need to submit explanations separately.
- Course project.
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- Students work individually on a Kaggle competition (details later) or a project creating a public-facing blog introducing and explaining a paper or a series of papers on AI for Social Good (that is not fully covered in class).
- Kaggle Competition
- Students will participate in one of the following Kaggle competitions (or a different one with the approval of the instructor), which involve real-world data and require practical application of ML techniques.
- Competitions
- Students are expected to analyze the problem and dataset provided in their chosen competition and propose appropriate ML techniques and strategies to solve the problem. Students can discuss multiple approaches and explain the rationale behind selecting a specific method.
- The grading scheme is similar to the 12-unit session, except that we have a lower expectation on all criteria (e.g., novelty)
- Blog Post
- The final version of the blog post will be published on the course website as a separate webpage
- The blog post is aimed to introduce and explain a paper or a series of papers on AI for Social Good
- Target audience: The post should be written at a level so that any senior undergraduate student majoring in computer science can have a good understanding of the overall societal challenge, the problem statement, the AI method, how the AI method helps address the societal challenge, the evaluation, and the impact. In addition, a big portion of the post (at least 50%) should be easily understandable by the general public.
- Content: The blog post must present a self-contained, cogent, and engaging narrative on this line of research, including a blend of scientific (high-level) and technical exposition.
- Figures/gifs/tables/videos that add to the exposition and enhance understanding are needed
- Bibliography optional but inline references to attributions made in the text should be given
- Submission format: markdown (preferred) or pdf
- Example blog posts:
- https://blog.research.google/2020/01/using-machine-learning-to-nowcast.html
- https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html
- https://research.facebook.com/blog/2023/4/every-tree-counts-large-scale-mapping-of-canopy-height-at-the-resolution-of-individual-trees/
- https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/
- https://deepmind.google/discover/blog/evaluating-social-and-ethical-risks-from-generative-ai/
- https://deepmind.google/discover/blog/codoc-developing-reliable-ai-tools-for-healthcare/
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- Students will work in small groups (1-3 students in each group) on a project exploring the possibility of using AI to help address a social good problem. The students are expected to focus on one or more societal challenges, propose models and AI-based solutions to tackle the challenges and evaluate the solutions.
- Grading Criteria: Please check slides 55-75 of the overview slide, or Canvas assignment description for the corresponding criteria for progress report, presentation, and final report.
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Grades. Students will be assigned final letter grades according to the following table.
| Grade | Range of Points | |--------|----------------------------------------------------| | A | [90,100], A-: [90,93) A: [93,97) A+: [97,100] | | B | [80,90), B-: [80,83) B: [83,87) B+: [87,90) | | C | [70,80), C-: [70,73) C: [73,77) C+: [77,80) | | D | [60,70), D: [60,67) D+: [67,70) | | R (F) | [0,59) |
Grading Policies
- Late-work policy: All late submissions will be graded with a 0.7 discount.
- Re-grade policy: To request a re-grade, the student needs to make a private piazza post titled “Re-grade request from [Student’s Full Name]” visible to instructors within one week of receiving the graded assignment.
- Attendance and participation policy: Attendance and participation will be a graded component of the course. The grading of the class participation will be mostly based on attendance, checked by in-class quizzes and asking and answering questions in class. Other factors include asking and answering questions on Piazza.
Course Policies
- Academic integrity & collaboration: For both paper reading assignments and written answer assignments, a student can discuss with other students, but they need to specify the names of the students they discussed with in the submission or comment area of the submission and complete the calculations, summary, and questions on their own. For the course project, the students can discuss and collaborate with others (including students, faculty members, and domain experts), but the students need to give proper credits to whoever is involved and report the contributions of each group member in the final report and presentations, which will be considered in the grading. For assignments and the course project, it is allowed to use publicly available code packages but the source of the code package needs to be specified in the submission. Plagiarism is not allowed. The policy is motivated by CMU’s policy on academic integrity which can be found here.
- Mobile devices: Mobile devices are allowed in class. Cellphones should be in silent mode. Students who use tablets in upright positions and laptops will be asked to sit in the back rows of the classroom.
- Accommodations for students with disabilities: If you have a disability and require accommodations, please contact Catherine Getchell, Director of Disability Resources, 412-268-6121, getchell@cmu.edu. If you have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate.
- Statement on student wellness: As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus here. Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.