- 10 reviews
- 10 completed
This is the continuation of Algorithms, Part I. Great course, like the Part I. You'll learn string and graph algorithms, as well as some more theoretical aspects like problem reduction, and complexity classes. You probably should take the Part I before taking this, because the string and graph algorithms sometimes used the basic data structures covered in Part I.
Great lecture videos. The lecturer uses pictures and animations to teach the data structures and algorithms that make the explanations very clear. Some proofs of the algorithms' performance are included. The code presented in lectures is very clear and clean. The programming assignments are quite tough and fun, and only differ slightly than the Princeton's equivalent. The downsides are the exercises, which require us to simulate some aspects of the algorithms by hand. On each try, the questions are new, so if you only have 1 mistake in previous try, you have to do it all over again, which is frustrating. The job interview questions are more interesting than the exercises, although they aren't graded. There's also no statement of accomplishment, and you even won't find your final points in your Coursera course records. Definitely the course to take when you first tackle this subject.
I took this course without prior knowledge of statistics. This course will introduce you to concepts in statistics. The "bio" part of this course only appears in examples, so don't take this course in hope to extend your knowledge in the biology part. The videos are quite good, the lecturer introduces the concepts using formal definitions, and I find it not as boring as it may sound. The lecturer frequently makes interesting asides and also warn the students on some subtle differences that may cause confusion. Sometimes the lecturer kinda assume that you've already seen some of the concepts he shows, so if you're new to this, sometimes you have to do some web search to really get what he taught. The quizzes and exams are not too challenging. Overall, this course is a good one to take if you want to get to know concepts in statistics in a mathematical way, but maybe it is better if you've already familiar with statistics and want a refresher course.
I took this course when it premiered. While Prof. Ullman is a brilliant man, the length of the video (30+ minutes), and maybe my lack of math maturity made me hard to follow the lectures. He most of the time just reads what's on the slide. It would be better if he write on the slides, like Andrew Ng did in Machine Learning or Jennifer Widom did in Intro to Databases, or like Salman Khan. The good parts are Prof. Ullman and his assistant would answer questions on the forum. The office hours also help. The quizzes and exam are quite difficult for me. The optional problems are difficult too. You should have experience with formal proof, because the course video is structured as a theorem-proof sequence. You should also read the book, because it provides more context and intuition than the videos.
This course is a good introduction to Machine Learning. You will be exposed to a handful of supervised and unsupervised learning algorithm. The professor really did a good job explaining concepts without assuming his audience have background in calculus or linear algebra. The programming assignments are fun, but not really difficult. The downside of this course is the lack of math. If you're looking for hardcore or rigorous introduction to ML, you won't find it here. But if you just want to survey ML algorithms and some best practice advice, know some programming, and don't really know calculus and linear algebra, this is for you.
This is a great course. I entered this course only knowing how to integrate and differentiate polynomials, and now I know so much more about calculus. This course is heavily based on Taylor series. Topics include limits, differentials, integrals, and their applications like newton's method, ordinary differential equations, averages, probability, and many more. The course ends with "discrete calculus", the analogue of calculus in the discrete world. The hand-drawn animated videos are excellent. The homework sets, though aren't graded, are challenging and essential to really understand the subject and clear up some misunderstanding that you may have if you only watch the lectures. There are also graded quizzes and timed final exam. This course isn't for people who haven't seen calculus before (as stated in prerequisites), or want a rigorous treatment of calculus. But if you know how to integrate or differentiate simple functions, or just want to brush up your calculus skill, you have to take this course.
This course is amazing. I had encountered SQL before, yet I learned so much from it. Things like relational algebra, normalization, OLAP, and recursion in SQL. I learned a ton. You should be prepared to work hard because this is not a walk-in-the-park course. The quizzes are challenging and really fun. It has many optional quizzes to help sharpen your understanding, and the videos are just great. There are also timed mid and final exam. This course is a must if you want to know about databases.
This course, while it teaches many interesting things, has many downsides. The professor just read the slides monotonically. I prefer Salman Khan or Andrew Ng style where they would write on the slides to get the point across. The videos are mostly just incomplete version of the notes. The notes are quite good, but if you've never encounter formal logic before, maybe it's hard for you to try to understand what's the motivation or intuition behind what's taught. The proof-assistance software on the other hand is great, and with the notes, the only positive things about this course. If you haven't encounter formal logic before, like propositional logic, this course really isn't for you.
The lecturers explain very clearly the concepts in the videos. The programming assignments is challenging and really fun, though it's still doable even if you don't have any experience with C, like me. Unfortunately, there's no autograded quiz to test conceptual understanding. This course teaches many things, like data types representation in memory, how function calls work, memory allocation, etc. The contents are great, but sadly the course is managed poorly, and maybe the worst I have ever seen in Coursera. This course can serve as a good foundation to systems programming and operating systems course.
I had some experience with Rails and Ruby before I took this course. The videos are great, the lecturers explain the concepts quite clearly, although sometimes Prof. Fox talks too fast, and the pacing of the materials is quite fast. The quizzes are easy, but the programming assignments can get challenging especially sometimes the instructions aren't quite clear. If you have a little experience with Ruby and Rails, you'll do great. If not, you must be prepared to learn SOA, REST, Ruby, Rails, TDD, and BDD in just 6 weeks.