Project TopicsYour first task is to pick a project topic. If you're looking for project ideas, please come to project office hours, and we'd be happy to brainstorm and suggest some project ideas.In the meantime, here are some that might also help.Most students do one of three kinds of projects:. Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it. Algorithmic project.
Machine-learning-projects has 7 repositories available. Follow their code on GitHub. Machine-learning-projects has 7 repositories available. Follow their code on GitHub. Machine-learning-recipes Following along with: Machine Learning Recipes with Josh Gordon tutorial-code machine-learning-recipes Python 80 144 0 0 Updated Mar 10, 2018. Jul 17, 2018 Top 10 Machine Learning Projects for Beginners. We recommend these ten machine learning projects for professionals beginning their career in machine learning as they are a perfect blend of various types of challenges one may come across when working as a.
Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it. Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.)Some projects will also combine elements of applications, algorithms and theory.Many fantastic class projects come from students picking either an application area that they're interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you're excited about.
(Just be sure to ask us for help if you're uncertain how to best get started.)Alternatively, if you're already working on a research or industry project that machine learning might apply to, then you may already have a great project idea.A very good CS229 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS229, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent machine learning research papers. Two of the main machine learning conferences are ICML and NIPS.
You can find papers from recent ICML conferences online:. All NIPS papers are online, at. Finally, looking at class projects from previous years is a good way to get ideas.Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as:.Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time.
We still expect a solid methodology and discussion of results, so pace your project accordingly.Notes on a few specific types of projects:. Deep learning projects: Since CS229 discusses many other concepts besides deep learning, we ask that if you decide to work on a deep learning project, please make sure that you use other material you learned in the class as well. For example, you might set up logistic regression and SVM baselines, or do some data analysis using the unsupervised methods covered in class. We may grade these projects using different criteria to make sure that grading is fair for students who have not had exposure to DL before. Finally, training deep learning models can be very time consuming, so make sure you have the necessary compute. Preprocessed datasets: While we don't want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning.
Hence if you choose to use preprepared datasets (e.g. From Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem. Replicating results: Replicating the results in a paper can be a good way to learn. However, we ask that instead of just replicating a paper, also try using the technique on another application, or do some analysis of how each component of the model contributes to final performance.Project Parts: Proposal, Milestone, Poster, & Final ReportThis section contains the detailed instructions for the different parts of your project.Submission: We’ll be using Gradescope for submission of all four parts of the final project.
We’ll announce when submissions are open for each part.You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.EvaluationWe will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the poster and final report will combine to be the majority of the grade. Projects will be evaluated based on:. The technical quality of the work. (I.e., Does the technical material make sense?
Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?). Significance. (Did the authors choose an interesting or a “real' problem to work on, or only a small “toy' problem? Is this work likely to be useful and/or have impact?).
The novelty of the work.
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You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.AI is transforming multiple industries.
After finishing this specialization, you will likely find creative ways to apply it to your work.We will help you master Deep Learning, understand how to apply it, and build a career in AI.