STATS 101C
Introduction to Regression 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
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 2019 - Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture). He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours. Homework is book problems, which generally aren't bad. Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
Fall 2019 - Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture). He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours. Homework is book problems, which generally aren't bad. Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
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