ENGR3105 · Engineering Human Centered
Morrissey College of Arts & Sciences
This course integrates mathematical foundations with engineering applications to provide an understanding of machine learning. Students will apply fundamental mathematical conceptssuch as probability, linear algebra, and optimizationto formulate and solve machine learning problems. Through hands-on MATLAB exercises, students will preprocess, explore, and analyze data, implement classification and regression models, and evaluate their performance using mathematical metrics. The course also explores the theoretical and practical aspects of deep learning, including backpropagation, convolutional neural networks (CNNs), and transfer learning, while emphasizing optimization techniques for improved model accuracy. By interweaving mathematical theory with engineering applications, this course ensures a well-rounded approach to machine learning, preparing students to tackle real-world challenges with both analytical and computational skills.
Course experience
Averages use the original five-point historical evaluation scale.
Organization
4.5 / 5
How well the course was organized
Challenge
4.4 / 5
How intellectually challenging students found it
Attendance
4.2 / 5
How necessary attendance was
Assignments
4.5 / 5
How helpful assignments were
Weekly effort
~3
hours per week
Estimated from the original workload response buckets. Individual sections may differ.
Instructor options
Ratings below reflect only recovered evaluations connected to this course.
Across time
Section-level results available in the recovered archive.
Spring 2025
2 sectionsFall 2024
2 sectionsSpring 2024
3 sections