CS 175: Project in AI Spring 2021
For assignments you are allowed to discuss the assignments verbally with other class members, but you are not allowed to look at or to copy anyone else's written solutions or code. All problem solutions and code submitted must be material you have personally written during this quarter, except for any standard library or utility functions.
For class projects all reports submitted must be written by you or members of your project team. Code generated for class projects can be a combination of code written by team members and publicly-available code. You should clearly indicate in your reports and in your code documentation which parts of your code was written by you or your team and which parts of your code was written by others.
Academic honesty is a requirement for passing this class. Any student who compromises the academic integrity of this course is subject to a failing grade. The work you submit must be your own. Academic dishonesty includes, but is not limited to copying answers from another student, allowing another student to copy your answers, communicating exam answers to other students during an exam, attempting to use notes or other aids during an exam, or tampering with an exam after it has been corrected and then returning it for more credit. If you do so, you will be in violation of the UCI Policies on Academic Honesty (see link). It is your responsibility to read and understand these policies. Note that any instance of academic dishonesty will be reported to the Academic Integrity Administrative Office for disciplinary action and may be cause for a failing grade in the course.
- Course description
Projects in Artificial Intelligence. Students in this class will work in small teams (2-3 students each) to develop artificial intellegence and machine learning algorithms and apply them to solve real-world problems. This quarter, we will work on detecting and tracking hands from images and videos. To fully illustrate the entire cycle of a machine learing project, we will start from data collection, move on to model building and training, and finally end with model evaluation and real-world applications. Throughout the process, we will review and learn various machine learning algorithms including logistic regression, SVM, and deep neural nets. We will also review popular machine learning tools and packages, such as sklearn, open CV, tensorflow and pytorch. The programming language used in this class will be based on Python.
- Focus of this quarter
Deep learning models such as CNNs, RNNs, Transformers, VAEs and GANs, and their applications.
- Lecture schedule
CS171 and CS178
- Grading Policy
- Assignments: 30%
- Course Project: 65%
- Project Proposal: 10%
- Presentation: 25%
- Final Project Report: 30%
- Participation: 5%
- Class participation, Polls, Online Discussions, etc
- Textbooks (not required)
- Machine learning by Kevin P. Murphy
- Deep Learning by Goodfellow, Bengio and Courville
- Academic Honesty