Fragouli is pretty straightforward with her teaching style and the material she covers. Although there can be some tricky questions, there's nothing unfair about her tests or the class in general.

DO NOT GIVE UP IN THIS CLASS!!!! I got a very bad grade on the first mid-term, however, the professor takes the highest among the three tests including the final so if you do bad on the first mid-term, don't get panicked, just prepare for the second mid-term, if do bad on this, prepare for the final, no matter what do not give up in this class, you have a good chance of improving your grade. The professor assigns extra-credit project , DO IT!!, it is very helpful, especially if you have bad grade from the first or the second mid-term. Exams are very though, but as long as you do the homework and the exercises you will be fine. GOOD LUCK!!!

Material was quite straight forward, almost a mirror to the material covered in EE102. But damn were the exams hard. Averages for the exams were always in the 50-60 range. The homeworks were pretty balanced in difficulty and I found it helpful to go over homeworks and discussion problems for exams. She also gives an extra credit assignment at the end of the class and is something you should definitely do seeing the exam averages were quite low.

I got a score in the low 70s on the first midterm, which was a little bit above the average of 66. For the second midterm, I got slightly below average, which was a 52. For the final, I got in the low 70s which was around the top 30% with an average of about mid 50s.

HWs = 10 points, Midterm1 = 15 points, Midterm2 = 25 points, Final = 50 points,
Bonus Project = 5 points.

One, or both of your midterms can be replaced by the final exam. Your grade is the best of the grading schemes.

The midterms often involved a few questions that were math/proof heavy. About 1/3 of the points would be determined by easier/more fair problems, but then 2/3 of the grade would be determined by a tricky proof like question, which was a bit unfair. The final exam was a little better and a little less weighted on the proofs and by then, we were a bit more used to the proofs.

Overall, the professor explains things pretty clearly. She is probably the best out of all the professors that teach this class and is probably the most generous in terms of grading. I know people who did average in the class or slightly below average and got a B+. The only thing I'd say I didn't like about the class was that the midterms were very math/proof heavy. Homeworks were hard, but I ended up getting pretty much full credit on all of them.

this class is bull shit. you can get 20 points on the first two midterms, skip hw and ec, and still end up with an A

Chose this class as a last moment elective but that was one of the best decisions I made. The class is meant to teach you concepts which were vaguely discussed in Math 61. Its not a proof based class so you get much more practical understanding rather than only working on math side of things.

The class's grading is based on best 2 out of 3 quizzes (40%), 5-6 homework (30%), and final project made in a group of 2 (30%). The final project is the most interesting part of this class as it's based on improving the professor's own published analysis methods and coming up with something better or similar. For our project, we were optimizing medical tests in a population with certain inter community and intra community connections. It was an open-ended project and the final goal was to try improving the professor's method which she had published only a year ago. It was one of the most fun projects I have done at UCLA and was manageable to complete in a week's time.

I would highly recommend taking this class both for an easy A as well as learning something cool. Not to mention if you are a CS major, learning about graphs always helps for interviews. There are 3 kinds of extra credit in the class: 1% for course evaluation, 2% for 3 most active students on campuswire, and 5% to the 2 best group projects announced at the end of the class.

The quizzes are tough and take some good practice and revision. The classes were recorded, and the slides were well organized, so revision was never a problem.

Fragouli is a good professor. There are no pre-requisites for this class. Every lecture, she goes over a different concept of graph theory, roughly following the chapters in Graph Theory with Applications by Bondy and Murty. The textbook is not required: she posts PDFs of relevant sections. She starts off by describing the mathematical theory, sometimes proving a theorem, and demonstrating how the theorem can be applied in real life. Homeworks were mostly similar to graph-theory questions you might find in the GTWA text. There was one coding homework, and two graph-theory projects. The project guidelines weren't totally clear and getting started can be tough if you don't have experience with coding. Osama the TA was a bit slow to respond. Fragouli herself is a very good lecturer, though the class was recorded at 8AM and by the end of the quarter fewer than ten students were showing up to live lecture...

We had two quizzes, but the second one had a technical issue and the professor gave everyone full points. The projects seemed to be graded pretty leniently.

Highly recommended for any engineering major who is thinking of taking an EE tech breadth.

Professor Fragouli is certainly one of the best professors I've had. I went from having little interest in this course and simply wanting to complete this requirement to having a genuine passion for it and being excited for 113.

Her lectures are clear, with an emphasis on why certain definitions are as they are. She usually goes over a proof first, followed by a few examples. I will say, however, that the homeworks in this course are more difficult than the lecture examples, but that is where I recommend staying active on campuswire and attending office hours regularly.

Her exams are roughly equal in difficulty to homework and discussion problems. There will almost always be one or two tricky problems, but they will never be unfair. This is where I recommend that you review your notes (lectures are recorded and uploaded as PDFs) and asking questions whenever you're stuck on something.

Thank you to Semira, our TA this quarter, for her exceptional work and dedication to answering questions on campuswire. She was quick to respond and extremely helpful during office hours.

Professor Fragouli is one of the best professors in the ECE department, and I would highly recommend taking this class with her. Her lectures are crystal clear, and she posts her lectures and lecture notes online for review. Professor Fragouli is very approachable and always willing to help during office hours. While her tests are challenging, they're fair and graded with integrity. The tests won't be exactly like homework or discussions, as they are a bit harder, if you understand the concepts you'll do well. She also does a really good job of making systems and signals very interesting to learn about, as with some other professors, apparently this class can be pretty dry.

Genuinely one of my favorite professors at UCLA.

I enrolled in this class as a CS undergrad. This class is hard, but very manageable; Fragouli does a great job at breaking down the subject and the course notes are very comprehensive.

You do not need to be very good at linear algebra to succeed in this class (Math 33A is probably enough). You should be comfortable representing systems of equations as matrices and taking transposes of block matrices. Although nothing fancy is required in terms of linear algebra, this class is still math-heavy and mostly theoretical.

The homework can get difficult, but prepares you well for exams. If you invest enough time, you will do well. The TAs and discussion sections are also very helpful. There was a group project this quarter (up to 4 people) about applying linear programming to machine learning; you will have to write code and do a report.

The latter half of the class has an algorithmic focus, especially when it comes to Max Flow and its many variants. CS 180 will help, but not by much.