Professor
Bolei Zhou
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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.
AD
Most Helpful Review
Winter 2025 - The assignments are interesting, sometimes time consuming, in particular the last assignment and course project. The material is also very interesting, and quite a bit of it is SOTA toward the end. The lectures are OK, the professor mainly reads from slides, but he is clearly passionate about the topic. That said, the exams are either intentionally designed for you to fail and generate a curve, or designed and evaluated in the laziest manner possible. If you do not EXACTLY match what their predefined rubric states, you will receive a zero. This includes rederiving formulas which were derived in class (on an open-note exam!) and matching exact keywords in short answer problems. On top of this, the course staff chose deliberately to hide the correct solutions and rubrics on Gradescope to discourage requesting regrades on solutions which appeared to be fully correct. The final grade is also not curved whatsoever (had 89% raw score, received a B+, scored over 100% on all assignments/project). So your entire grade comes down to this sketchy exam where despite knowing 80-90% of the material, you can easily receive a 60% or lower. If you value your GPA, audit this class. Material is interesting, projects are great, but dealing with this nonsense final is not worth the risk.
Winter 2025 - The assignments are interesting, sometimes time consuming, in particular the last assignment and course project. The material is also very interesting, and quite a bit of it is SOTA toward the end. The lectures are OK, the professor mainly reads from slides, but he is clearly passionate about the topic. That said, the exams are either intentionally designed for you to fail and generate a curve, or designed and evaluated in the laziest manner possible. If you do not EXACTLY match what their predefined rubric states, you will receive a zero. This includes rederiving formulas which were derived in class (on an open-note exam!) and matching exact keywords in short answer problems. On top of this, the course staff chose deliberately to hide the correct solutions and rubrics on Gradescope to discourage requesting regrades on solutions which appeared to be fully correct. The final grade is also not curved whatsoever (had 89% raw score, received a B+, scored over 100% on all assignments/project). So your entire grade comes down to this sketchy exam where despite knowing 80-90% of the material, you can easily receive a 60% or lower. If you value your GPA, audit this class. Material is interesting, projects are great, but dealing with this nonsense final is not worth the risk.
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