COM SCI 188
Special Courses in Computer Science
Description: Lecture, four hours; discussion, two hours; outside study, six hours. Special topics in computer science for undergraduate students taught on experimental or temporary basis, such as those taught by resident and visiting faculty members. May be repeated once for credit with topic or instructor change. Letter grading.
Units: 4.0
Units: 4.0
3 of 3
AD
Most Helpful Review
Winter 2024 - As the lead professor of a Computer Vision research lab in UCLA, Professor Zhou is extremely knowledgeable in the field. While lecture slides are dense with content, they provide a brilliant overview of Deep Learning and CV, including state-of-the-art models such as ResNet, Vision Transformers and Diffusion. Homework assignments are challenging but fun, where we had to make use of PyTorch to build above-mentioned models from scratch. Whenever I struggle to debug, my TA, Zhizheng, was really helpful on Piazza. There was also a final project where we had to read research papers and compare 3 different CV models, which was a great opportunity to gain an in-depth understanding of CV models. The only downside was the tough final exam, but it turned out well eventually as he curved our overall grades. For anyone interested in Deep Learning and Computer Vision, I would highly recommend this class. Take note that while there are no enforced pre-requisites, this class does require you to have substantial prior knowledge in Machine Learning, Multivariable Calculus and Linear Algebra. Otherwise, this class could be quite challenging and fast-paced.
Winter 2024 - As the lead professor of a Computer Vision research lab in UCLA, Professor Zhou is extremely knowledgeable in the field. While lecture slides are dense with content, they provide a brilliant overview of Deep Learning and CV, including state-of-the-art models such as ResNet, Vision Transformers and Diffusion. Homework assignments are challenging but fun, where we had to make use of PyTorch to build above-mentioned models from scratch. Whenever I struggle to debug, my TA, Zhizheng, was really helpful on Piazza. There was also a final project where we had to read research papers and compare 3 different CV models, which was a great opportunity to gain an in-depth understanding of CV models. The only downside was the tough final exam, but it turned out well eventually as he curved our overall grades. For anyone interested in Deep Learning and Computer Vision, I would highly recommend this class. Take note that while there are no enforced pre-requisites, this class does require you to have substantial prior knowledge in Machine Learning, Multivariable Calculus and Linear Algebra. Otherwise, this class could be quite challenging and fast-paced.
3 of 3
