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
Most Helpful Review
Spring 2017 - Highly recommend Prof. Scalzo. Really nice guy, and he does a good job of explaining concepts. I believe this class is now a regular CS class and not a 188 anymore, but when I took it basically all you did was a quarter long project and a final exam. The final was just memorization of concepts covered in his slides, and for the project you could pretty much do anything with machine learning and medical data. So the workload was basically up to you: you could either just do a really simple project, or if you had a personal interest you could go for something more ambitious. Either way I think he tended to grade on the easier side, especially if he saw you were putting effort into it. Highly recommend this class if you want to learn about machine learning with medical imaging and do a practical project.
Spring 2017 - Highly recommend Prof. Scalzo. Really nice guy, and he does a good job of explaining concepts. I believe this class is now a regular CS class and not a 188 anymore, but when I took it basically all you did was a quarter long project and a final exam. The final was just memorization of concepts covered in his slides, and for the project you could pretty much do anything with machine learning and medical data. So the workload was basically up to you: you could either just do a really simple project, or if you had a personal interest you could go for something more ambitious. Either way I think he tended to grade on the easier side, especially if he saw you were putting effort into it. Highly recommend this class if you want to learn about machine learning with medical imaging and do a practical project.
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
