UCLA Department of Bioengineering
BIOENGR 188: Machine learning & data-driven modeling in bioengineering
Instructor: Prof. Aaron Meyer (email@example.com), 4121G Engineering V
Class Hours: Lecture: Tuesdays/Thursdays, 2:00 – 3:50pm (Boelter Hall 5420)
Lab Section: Fridays, 2:00 – 3:50 pm (Bunche Hall 3153)
Teaching Assistant: Yi Luo (firstname.lastname@example.org)
Required: None. Handouts will be provided as needed and posted on the website. Optional/Helpful Other Resources:
Efron & Hastie (2016). Computer Age Statistical Inference.
VanderPlas (2016). Python Data Science Handbook.
Hastie et al. (2009). The Elements of Statistical Learning.
MacKaye (2005). Information Theory, Inference and Learning Algorithms.
Manipulating biological systems requires techniques to interpret complex measurements. This project-based class will introduce techniques for inferring biological meaning from experimental measurements using computational and analytical techniques. The objectives of this course are (1) to give students a working knowledge of techniques for rigorously analyzing complex data sources, (2) to illustrate the frontier and open challenges in computational systems biology and bioengineering, and (3) to ensure familiarity with the necessary tools to effectively apply computation as part of an individual or group research effort. Lectures will introduce foundational applied machine learning and statistics techniques. Laboratory session will involve hands-on implementations from recent literature. Homework will be primarily project-based using recent literature-derived applications. There will be a midterm exam and final design project. The final projects will involve novel analysis of data derived from the literature using techniques from the course. Instructors will guide focus and development of the project.
By the end of the course you will have an increased understanding of:
The best way to contact me outside of class is by email. Students should be aware that I will not answer emails arriving after 10:30 PM or before 8:30 AM, and should not be counted on to answer emails immediately. I am happy to meet with students outside of class time to discuss questions or concerns. Please see me after class or contact me by email to set up an appointment.
This class is graded on a combination of exams, small project implementations, and a larger final project. Students earning high grades will have demonstrated the following:
Come, think critically, challenge yourself, and you will do well.
In our classroom, we will rely on each student being engaged in the conversation. To minimize distractions, please silence all phones. Computers are an extremely valuable research and learning tool and are encouraged in class. Please minimize use of your computer for unrelated activities, however, to avoid distractions.
30% ~ Final Project
30% ~ Midterm
20% ~ Homework Assignments
20% ~ Class Participation
Mid-way through the quarter, I will give each of you some feedback on your progress in the class. I also welcome freedback from you then or any other time. Please come speak with me if there are any issues about material in class or teaching style.
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