Special Courses in Computer Science: Data Science Fundamentals

Description: Lecture, four hours; discussion, two hours; outside study, six hours. Special topics in computer science for undergraduate students taught on experimental or temporary basis, such as those taught by resident and visiting faculty members. May be repeated for credit with topic or instructor change. Letter grading.

Units: 4.0
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Overall Rating 4.0
Easiness 3.5/ 5
Clarity 3.2/ 5
Workload 3.5/ 5
Helpfulness 3.5/ 5
Most Helpful Review
Winter 2021 - This class is a pretty useless class, since there is a huge lack of practice material. It doesn't help that the homeworks barely test on what is covered in class, leading to like 5 people attending live lecture everyday, while there are 100 people in the class. This class is focused on a bunch of ML models, like M146, except there are no derivations, and you're basically just using models from sklearn / other ML libraries, and running them to see results. To me, this seemed extremely stupid, since I had no idea what any of the models were doing inside. Also, we never even learned how to do random search or grid search for hyperparameter tuning, which made this class even stupider. Honestly, this class material could be learned in like 1 week if there was a good textbook and syllabus to follow, since we barely covered anything in depth in the homework or the tests. There were only 3 homeworks and 3 projects, each probably took less than 2 hours, so a very light workload. The exam was the worst part of this class. If we actually knew anything, the exam would be easy, but because of no practice problems in lecture, like 1 practice problem in discussion, and no textbook, it was impossible to practice for the exam. If there was 1 practice exam, I would have understood what I was weak on .. but no, hence the exam was hard even though I could have studied all the relevant practice problems in like 30 minutes. Also, Piazza communication is super weak here, questions were left unanswered for weeks and hastily answered before the final. Not a good look. All in all, a class not worth taking. If you want to learn how to implement ML models, spend like 5 minutes on sklearn. If you want to learn the inner workings of basic ML models, take M146 (you def do NOT learn it in this class). If you want to learn the inner workings of neural networks, take ECE 247, or spend 15 minutes watching a 3 blue 1 brown video. It's not even an easy A since the test at the end is a total crapshoot and worth 40% of your grade; if you want an ez class take CM122. Rant over!
Overall Rating 1.2
Easiness 3.5/ 5
Clarity 1.0/ 5
Workload 3.0/ 5
Helpfulness 1.0/ 5
Most Helpful Review
Spring 2021 - This professor does not give a shit about the students at all. No matter what he says or did throughout the quarter, he does not care at all and you should NOT take it with him - AVOID AT ALL COSTS. He emphasizes understanding the content over memorization and that as long as you make sure to review his lectures, complete the projects, complete the homeworks, and study the sparse resources he gave us (1 practice exam that he didn't write), you will do well in this class. This is a complete and utter lie. I aced the homeworks and projects alike as I understood all the content he went over in class, and then this horrible, uncaring professor goes ahead and on an exam worth 40% of our grade, puts questions that have us doing calculations he never even mentioned in class, says that the exam will be as long as or shorter than the practice exam in terms of number of questions, and then goes ahead and makes it much longer, and only gives us 90 minutes for the exam (which is worth the same as a final exam which is generally 3 hours and gives us half the time). This professor hands down does not give shit about the students. If it wasn't for the TAs seeing how shitty the professor was and making sure to give out extra points, a lot of people would be leaving the class with Cs SOLELY because of the final. I have never had such a hypocritical and uncaring professor at UCLA and I feel disgusted. Even Eggert makes sure to at least curve the class at the end so even if you get screwed by the scores, you have a chance to end up with a better grade than you expected. I know some people will downvote this answer, and I can tell you it will likely be those students who took CSM146 as they were already taught the content on the questions that Majid never taught us and thus easily aced the final (Side note, it is because these students did well that Majid ended up not curving because in his inane and broken logic, its fair to put questions only those with previous experience in an ML course could answer). In conclusion, this professor is truly terrible and avoid taking ANY class with him if possible (his CS 180 class was dogshit as well: he down curved the class after explicitly saying he wouldn't).
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