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• 3 Credit Hours
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Best course I've taken so far. Bottom line: this will teach you the math and coding behind most basic machine learning algorithms. It's essentially the flagship data science class. Easier to take it after ISYE 6501 and CSE 6040. This class was probably harder before chatGPT, but will still challenge you. Chat cannot do the assignments - it will get stuff wrong and you have to double check it, so even if you opt to use chat you end up learning a lot.
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Great course! I think it's a must! Great intro to various ML applications. I combined it with MGT 8803 which made the time commiment restricted. I wish i had more bandwidth to provide towards this course. It was a tough course for me because of lack of bandwidth but overall a great course. Make sure to find a good teammate for the project.
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Very interesting (and in my opinion, time consuming) course. It has a perfect balance between theory and applications (as all the homework involved both some mathematical proofs and the implementation of algorithms). The first part of the course is a bit scary, as it asks you to implement algorithms from scratch, and it's not that easy when you need to build something that can't take too long to run. After the second or third homework, it gets easier, specially because you get used to what is being asked. With that in mind, I'll leave some tips here:
I had high hopes for this class based on its reputation but was ultimately left disappointed. I expected more coding.
Roughly 50% of the homeworks require proofs. The word "computational" is in the course title and I felt this was too much theory to be graded on.
The homeworks in the second half contained more "compare and contrast different models" versus implementation. I feel this is already done in other classes like 6501.
The homeworks take a very long time to get graded; at least a week and sometimes two weeks. This could be improved by using Gradescope, which I believe they are trying to transition to. There were a few chances for extra credit to submit to Gradescope for their testing.
TAs are very responsive on the boards.
The lectures are very informative.
The project was fun and a chance to explore without worrying about getting perfect results.
This course has great potential, but it was run like a poorly run start-up, and I really wish they'd hire some course consultants to revamp it. Assignments are fine, TAs are fantastic, and despite the poor audio-quality, the lectures are quite good as well. There's just so much unnecessary work down by TAs and students, and I think it's due to poor management.
Chat GPT Professor needs to genai proof this course. Perhaps include proctored exams (coding & theory). Make part of the grade dependent on model performance to really force students to finetune hyper params (could be a standalone assignment). Perhaps use genai yourself to provide a baseline grade on assignments to free up TAs more. Adding gradescope + tests will have the same effect.
Professor Yao Xie She actually is one of the few professors who's quite involved in the course and frequently offers OH and mass-emails students. She also clearly knows what she is talking about.
This course, being 100% coursework and project-based, provides a rewarding learning experience, particularly for those new to computational analytics. While the homework assignments require significant time and effort, especially early on, the investment is well worth it. Prof. Yao Xie delivers highly effective lectures, clearly explaining key machine learning algorithms and their implementation, and her weekly office hours are greatly valued. The TAs are helpful and responsive, although the large number of TA sessions can feel overwhelming.
To be honest, for the first few weeks I'd look at the lectures and then try to figure out the homework but over the course of the semester I just gave up and went straight to the homework instead and use the lectures as a way to help me solve the homework questions. Id recommend buying ChatGPT plus for this course though, it saved me so much time to understand and break down the concepts. I felt like this course was notoriously one of the hardest but since the introduction of AI, it got a bit easier. Mind you, the work still needs to be your own and throwing AI responses to your homework will result in plagiarism. Use it to guide you. Proofs take a while to figure out too. AI can help you open up the door to solving the equation but you have to walk through it to solve it yourself.
Really learned a lot from this course from a theory to code implementation.
Homework: A total of 6 homeworks which make up 70% of your grades. Homeworks are a good balance between theoretical questions (think why would we use this algorithm / derive this equation) and practical coding questions (write up the KMeans algorithm from scratch). Homeworks are generally tougher for the first 3 and subsequently gets slightly easier. Start your homeworks early because it really is a time-sink as you also have to present the homework in a report format. Marking is generally lenient
Project: Project makes up the remaining 30%. In my opinion, the project seems easier than homeworks because you can use packages to solve the problem you have specified.
Pre-reqs needed: Linear Algebra and Multivariate calculus. Do not skimp on this because it definitely is needed for the homeworks in solving the theoretical questions.
I will highly recommend this course because it really does provide the theoretical rigour as well as the difficulty from coding.
This course teaches machine learning, involving both theory and applications, mostly uses bishop's book. .
This was a great course. A very strong range of algorithms which you get to code for each one (sometimes from scratch) with strong math proofs and demonstrations behind each topic. Don't stress about the math it's nothing too crazy just abstract derivatives (chains rules, log properties), Lagrangian multipliers (but the professor always goes over the math very well for each algorithm). While the course certainly covered these topics in depth, it wasn't the focus (about 20% of the points in HWs). The lectures were very well balanced in theoretical, math and practical terms. Unlike other courses where you never have to see the lecture videos, here it's an absolute must, it will help you immensely in doing the homework assignments (6 total), you also definitely should use the starter code skeletons for each topic and homework assignment. You learn about a bunch of machine learning and stats algorithms, from an overview of what they are, do and used for, to the their objective function, what they optimize and finally how to use it in Python or matlab. Course deals a lot with images, so a lot of the time you aren't working with traditional dataframes rather their favorite is a series of Yale face pictures reduced in dimensions for modelling. I had never worked with images so learning how to get rows and columns from a picture was tricky but it's not too bad if you have worked with Python before. This was my third course i took it simultaneously with DataViz and Computing for Data Analytics. Definitely a mistake you should deff take CfDA before this class and NEVER partner it up with DataViz, I got an 88 in this class which is a flat B. But I can genuinely say I learned a ton and it was worth the suffering.