Professor
Vwani Roychowdhury
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Most Helpful Review
Fall 2018 - For a supposedly simple class the professor made it very difficult. Lectures were not clear and have the class didn’t show up. He gave one more homework than on the syllabus plus the project was only a week long instead of the planned 3 weeks so instead of studying for finals everyone was trying to write the long lines of code needed for the vague project spec. Only good thing, nice grading.
Fall 2018 - For a supposedly simple class the professor made it very difficult. Lectures were not clear and have the class didn’t show up. He gave one more homework than on the syllabus plus the project was only a week long instead of the planned 3 weeks so instead of studying for finals everyone was trying to write the long lines of code needed for the vague project spec. Only good thing, nice grading.
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Most Helpful Review
Winter 2023 - This class is more or less a high level overview of machine learning. It covers common tools for analysis and feature extraction like dimensionality reduction and goes over common ML models. Some examples here include Naive Bayes, SVMs, decision trees, neural networks, etc. The coursework load is fairly light for an engineering class with 4 projects, which are long and reasonably well-guided assignments. These can be done in a group or alone, where I opted for the latter and would generally recommend that as it is quite doable and you learn more this way than by carving them up. Regarding the lectures and Prof. Roychowdhury, I generally found them a bit disorganised and did not engage with them much. Roughly 3 lectures in I pretty much focused only on the assignments and was fine. Said assignments I very much enjoyed though, as they were heavy on programming and analysis, which I wanted to practice. They were not particularly mathematically rigorous though, so I would recommend ECE 246 "Foundations of Statistical Machine Learning", by Prof. Diggavi for that.
Winter 2023 - This class is more or less a high level overview of machine learning. It covers common tools for analysis and feature extraction like dimensionality reduction and goes over common ML models. Some examples here include Naive Bayes, SVMs, decision trees, neural networks, etc. The coursework load is fairly light for an engineering class with 4 projects, which are long and reasonably well-guided assignments. These can be done in a group or alone, where I opted for the latter and would generally recommend that as it is quite doable and you learn more this way than by carving them up. Regarding the lectures and Prof. Roychowdhury, I generally found them a bit disorganised and did not engage with them much. Roughly 3 lectures in I pretty much focused only on the assignments and was fine. Said assignments I very much enjoyed though, as they were heavy on programming and analysis, which I wanted to practice. They were not particularly mathematically rigorous though, so I would recommend ECE 246 "Foundations of Statistical Machine Learning", by Prof. Diggavi for that.
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