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
1 of 2
Most Helpful Review
Fall 2024 - The professor is super cool and (at least in lecture) tries his best to keep students engaged. It's obvious that he's really knowledgeable and enjoys what he does. While the content itself can be complex, he structures the class so that it is (in my opinion) easy to get an A. To put this into perspective, I'm a Cogsci major who took this class as an elective and have only took one other Stats upper div, and I managed to get a high A. I still had to put in some effort, but I honestly feel like Stats majors/minors could probably do well in this class with minimal effort. To get an A+, I scored around the average on the homeworks, final, and Kaggle project and around the upper quartile for the midterm. While caring, the professor was often unclear, and the class itself was extremely disorganized. For some of the slideshows with ~100 slides, he only went over about half of the slides while skimming over the rest. Sometimes information given by the professor and the TA would conflict. While the final project was fun and applicable overall, the professor gave very little guidance for the presentation and the paper--only providing a single "sample" of each from previous quarters. Grading itself was not overly strict, yet it was slightly confusing. On some of the homeworks and the final, grades were reposted multiple times--sometimes going up or going down with little explanation as to the rationale for the changes. As this was my first machine learning class, I found the content very interesting. It's basically a survey of a whole bunch of supervised and unsupervised machine learning methods. You don't go super in-depth on specific topics, but you do learn a lot. So overall, poorly structured class but cool content and professor.
Fall 2024 - The professor is super cool and (at least in lecture) tries his best to keep students engaged. It's obvious that he's really knowledgeable and enjoys what he does. While the content itself can be complex, he structures the class so that it is (in my opinion) easy to get an A. To put this into perspective, I'm a Cogsci major who took this class as an elective and have only took one other Stats upper div, and I managed to get a high A. I still had to put in some effort, but I honestly feel like Stats majors/minors could probably do well in this class with minimal effort. To get an A+, I scored around the average on the homeworks, final, and Kaggle project and around the upper quartile for the midterm. While caring, the professor was often unclear, and the class itself was extremely disorganized. For some of the slideshows with ~100 slides, he only went over about half of the slides while skimming over the rest. Sometimes information given by the professor and the TA would conflict. While the final project was fun and applicable overall, the professor gave very little guidance for the presentation and the paper--only providing a single "sample" of each from previous quarters. Grading itself was not overly strict, yet it was slightly confusing. On some of the homeworks and the final, grades were reposted multiple times--sometimes going up or going down with little explanation as to the rationale for the changes. As this was my first machine learning class, I found the content very interesting. It's basically a survey of a whole bunch of supervised and unsupervised machine learning methods. You don't go super in-depth on specific topics, but you do learn a lot. So overall, poorly structured class but cool content and professor.
AD
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
AD
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 2024 - Honestly I can barely tell you a thing I learned in this class but somehow I pulled off a grade I am happy with and didn't struggle too much along the way. Prof. is extremely kind, funny, and means well... just did not learn much likely given I took the 101 series before the 100 or 102 which is my own fault. You don't have to go to class, but I did since he only posts unannotated slides. Anyways breakdown is as follows: 30% homework (5x 6% each) - coding of class concepts I did in python 40% midterm - in person exam consisting of multiple choice, true/false, short answer, proofs, general problems 30% final project - ours was on NBA data, pretty cool but can be tough with a random group if you don't know people in the class I definitely recommend to take since he grades off of class rank which obvi is how I got a decent grade despite struggling on the midterm (0-10% A+, 10-30% A, 30-70% A-, 70-80% B+, 80-90% B, 90-100% B- or lower). Plus you will have some good laughs along the way if you go to class.
Fall 2024 - Honestly I can barely tell you a thing I learned in this class but somehow I pulled off a grade I am happy with and didn't struggle too much along the way. Prof. is extremely kind, funny, and means well... just did not learn much likely given I took the 101 series before the 100 or 102 which is my own fault. You don't have to go to class, but I did since he only posts unannotated slides. Anyways breakdown is as follows: 30% homework (5x 6% each) - coding of class concepts I did in python 40% midterm - in person exam consisting of multiple choice, true/false, short answer, proofs, general problems 30% final project - ours was on NBA data, pretty cool but can be tough with a random group if you don't know people in the class I definitely recommend to take since he grades off of class rank which obvi is how I got a decent grade despite struggling on the midterm (0-10% A+, 10-30% A, 30-70% A-, 70-80% B+, 80-90% B, 90-100% B- or lower). Plus you will have some good laughs along the way if you go to class.
AD
1 of 2
