STATS 101C
Introduction to Statistical Models and Data Mining
Description: Lecture, three hours; discussion, one hour. Enforced requisite: course 101B. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2023 - I took him for Stats 101B and Stats 101C, and honestly, I don't think he's that bad. He is definitely an unclear lecturer, but supplies an abundance of slides (which lack adequate information), so you know what to study (I looked in the textbooks to solidify my understanding of the material). He is unresponsive, but TAs can usually answer most of your questions about homeworks and projects. For 101C, we had a midterm (in-person, VERY fair), a final (take-home, easy), and a group project on Kaggle where teams were ranked by performance (we were around 13/32 and got an A+, so I don't think grading was very harsh). We also had 6 homeworks, which varied in difficulty (all on R). At the end of the day, his tests are pretty easy, his projects are graded nicely, and he cares about his students doing well in his class. Although he is a bad lecturer, don't be afraid to take him!
Fall 2023 - I took him for Stats 101B and Stats 101C, and honestly, I don't think he's that bad. He is definitely an unclear lecturer, but supplies an abundance of slides (which lack adequate information), so you know what to study (I looked in the textbooks to solidify my understanding of the material). He is unresponsive, but TAs can usually answer most of your questions about homeworks and projects. For 101C, we had a midterm (in-person, VERY fair), a final (take-home, easy), and a group project on Kaggle where teams were ranked by performance (we were around 13/32 and got an A+, so I don't think grading was very harsh). We also had 6 homeworks, which varied in difficulty (all on R). At the end of the day, his tests are pretty easy, his projects are graded nicely, and he cares about his students doing well in his class. Although he is a bad lecturer, don't be afraid to take him!
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Spring 2016 - Gould is really nice and emphasizes understanding the intuition rather than the mathematical detail. The class is basically a walkthrough of many of the most popular machine learning algorithms. The downside is that you don't really learn how the algorithms are derived from. (You need another class for that) Homework and midterm were very easy when I took it. My favorite part about the class is the Kaggle competition which involves teaming up with classmates and competing to come up with a model that best predicts a dataset. There was no written final and the grade was based on your team's performance and the group presentation. I learnt the most from working on the project and there was no restriction on what models you could use. Fun times.
Spring 2016 - Gould is really nice and emphasizes understanding the intuition rather than the mathematical detail. The class is basically a walkthrough of many of the most popular machine learning algorithms. The downside is that you don't really learn how the algorithms are derived from. (You need another class for that) Homework and midterm were very easy when I took it. My favorite part about the class is the Kaggle competition which involves teaming up with classmates and competing to come up with a model that best predicts a dataset. There was no written final and the grade was based on your team's performance and the group presentation. I learnt the most from working on the project and there was no restriction on what models you could use. Fun times.
Most Helpful Review
First off LOL the picture posted on here is funny, I wouldn't take her if i could, almost every other Stats professor at ucla is better, not to say she's bad, the rest of the STATS prof are really good if you do take her, she might refer to a "book" a lot but dont bother reading it, anything she will test you on is based off her lecture notes memorize all her examples in class or on the lecture notes because her tests have problems from lecture and homework... almost all the problems on the tests you would have seen before Best of luck
First off LOL the picture posted on here is funny, I wouldn't take her if i could, almost every other Stats professor at ucla is better, not to say she's bad, the rest of the STATS prof are really good if you do take her, she might refer to a "book" a lot but dont bother reading it, anything she will test you on is based off her lecture notes memorize all her examples in class or on the lecture notes because her tests have problems from lecture and homework... almost all the problems on the tests you would have seen before Best of luck
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Most Helpful Review
Fall 2020 - I like the way Vazquez conducted the course, and I would recommend taking him if he is teaching the class. Grading consists of a homework assignment of 3-4 (+/ 2) textbook questions each week, and two equally weighted midterm and final Kaggle competition projects (which are a bit challenging, not so much because of the difficulty of the datasets but because of it being a competition within a class of so many intelligent students). The theme of his class seems to be practical application and job practice, which I appreciated. He is a clear lecturer and the way he interacted with students (especially students from abroad haha) was sweet. He records everything and attendance is not required.
Fall 2020 - I like the way Vazquez conducted the course, and I would recommend taking him if he is teaching the class. Grading consists of a homework assignment of 3-4 (+/ 2) textbook questions each week, and two equally weighted midterm and final Kaggle competition projects (which are a bit challenging, not so much because of the difficulty of the datasets but because of it being a competition within a class of so many intelligent students). The theme of his class seems to be practical application and job practice, which I appreciated. He is a clear lecturer and the way he interacted with students (especially students from abroad haha) was sweet. He records everything and attendance is not required.
Most Helpful Review
Fall 2022 - Very easy class, but I learned nothing. Lectures were pretty useless as they focused on highly theoretical concepts with very little practical applications. There was also very little room to learn during the homework assignments because they were a joke. This was disappointing as I anticipated this class to be one of the most important ones I would take. We didn't have a final exam and virtually had no homework. However, Shirong is very nice and I like him as a person. This was also his first time teaching a class at UCLA I believe, so I think with extra preparation on his part, he could teach this class in more applicable way in the future.
Fall 2022 - Very easy class, but I learned nothing. Lectures were pretty useless as they focused on highly theoretical concepts with very little practical applications. There was also very little room to learn during the homework assignments because they were a joke. This was disappointing as I anticipated this class to be one of the most important ones I would take. We didn't have a final exam and virtually had no homework. However, Shirong is very nice and I like him as a person. This was also his first time teaching a class at UCLA I believe, so I think with extra preparation on his part, he could teach this class in more applicable way in the future.
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