The goal of this course is to introduce students to the fundamental concepts and methods of personalized medicine and reinforcement learning. The course will cover the following topics:
There is no required textbook for this course. However, the following books can be useful, although many of the course material and results can be found in online lecture notes and research papers. Especially for personalized medicine, the field is still evolving and there is no single textbook that covers all the topics.
In addition, while developing this course, we will also refer to the following books and lecture notes. You may also refer to the papers cited in each lecture notes.
You need to be familiar with basic statistical theory, linear algebra, linear regression and some common machine learning models. We assume that you are able to utilize any models covered in STAT 432 to implement our introduced methods.
You also need to be familiar with basic programming in R
and Python. Lecture notes and homework assignments will be
provided in R Markdown and/or
Jupyter Notebook. You can use any programming language to
complete your homework assignments. However, you are responsible for
installing the required packages and libraries.
“As we explore the fascinating realm of programming and machine learning, I encourage you to utilize AI tools in your homework assignments. These cutting-edge technologies can provide you with insights, automate routine tasks, and enhance your learning experience. However, I must stress the importance of understanding both the capabilities and limitations of these tools. While they can be powerful aids, relying solely on them without a deep comprehension of the underlying principles can lead to misconceptions and errors. Be mindful of biases in data and the ethical implications of using AI in various contexts. Embrace these tools as an extension of your skills, but never substitute them for critical thinking and rigorous understanding. I believe that the responsible use of AI tools can greatly enrich your learning.”
The above paragraph was generated by GPT-4. The university also has a discussion about ChatGPT. If you have questions or concerns, please let me know or talk to an expert. A general rule of thumb is that you should be able to explain your answers in a clear and logical way. You will be penalized if your answers are not meaningful, especially when the final results are wrong.
Each homework consists 10% of your final score. A total of 50% points will be awarded for this section of the course. Your final grade will be determined by combining your scores in Prof. Liang’s section and my section.
The official University of Illinois policy related to academic integrity can be found in Article 1, Part 4 of the Student Code. Section 1-402 in particular outlines behavior which is considered an infraction of academic integrity. These sections of the Student Code will be upheld in this course. Any violations will be dealt with in a swift, fair and strict manner. Homework assignments are meant to be learning experiences. You may discuss the exercises with other students, but you must write up the solutions on your own. In short, do not cheat, it is not worth the risk. You are more likely to get caught than you believe. If you think you may be operating in a gray area, you most likely are.
The instructor reserves the right to make any changes he considers academically advisable. Such changes, if any, will be announced on this website and through email. Please note that it is your responsibility to keep track of the proceedings.