Computer Vision. Note: We will expect your final writeup to be on the same topic as your milestone. Of course, depending on the topic of This course also serves as a foundation on which more specialized courses and further independent study can build. Submission: We’ll be using Gradescope for submission of all four parts of the final project. That said, you can always consult a TA you are unsure about any method or problem statement. The team size will be taken under consideration when evaluating the scope of the project in breadth and depth, meaning that a three-person team is expected to accomplish professor) had advised you on this work, your write-up must fully acknowledge their contributions. The final project is intended to start you in these directions. The CSI Tool is built on the Intel Wi-Fi Wireless Link 5300 802.11n MIMO radios, using a custom modified firmware and open source Linux wireless drivers. 一、网易云课堂： 1、翁恺老师的计算机课程 翁恺个人主页 本身翁恺老师就是浙大计算机学院的优秀教师，在线上授课时间比较长，经验丰富，条理清晰，在保证授课效果的同时，声音也好听简直是大大加分。 2、大学计算机专业课程体系 大学计算机专业 这门课程最大的优点是体系性强。 Are the proposed algorithms or applications clever and interesting? ), The novelty of the work. Where homework assignments are divided into sections. Please include a section that describes what each team member worked on and contributed to the project. We will use Piazza for class discussion. Do the authors convey novel insight about the problem Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. Mihir is a Master's student in Data Science at the NYU Center for Data Science, interested in computer vision, reinforcement learning, and natural language understanding. So, if you'd like to combine your CS229 project with a class X but class X's policies This course covers a wide variety of topics in machine learning and statistical modeling. The term project is 40% of the final grade. Sreyas is a second year PhD student in the Data Science Program at CDS working with Prof. Carlos Fernandez-Granda and Prof. Eero Simoncelli. Method: What machine learning techniques have you tried and why? David is a data scientist in the office of the CTO at Bloomberg L.P. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Your milestone should be at most 3 pages, excluding references. The only difference is the \(g(z)\) used in the process. For shared projects, we also require that you submit the final report from the your project teammates, please create a private Piazza post. Built with lots of keyboard smashing and copy-pasta love by NirantK. You should make sure that you follow all the guidelines and requirements for the CS229 project (in addition to the requirements of the other class). So, pick something that you can get excited and passionate don't allow for it, you cannot do it. Junwon Park . CS229 Final Project Information. You should have tried at least one baseline. Digression - Perceptron. We still expect a solid methodology and discussion of results, so pace your project accordingly. In particular, we expect the team to submit a completed project (even for team of CS229 Problem Set #1 Solutions 2 The −λ 2 θ Tθ here is what is known as a regularization parameter, which will be discussed in a future lecture, but which we include here because it is needed for Newton’s method to perform well on this task. Also check out Google Colab for free GPU resources. Basic idea of Newton’s method; 1.2. into your final project. If you choose to collaborate No, the final report will be submitted via Gradescope. The choice of optimization algorithm for your deep learning model can mean the difference between good results in minutes, hours, and days. Other interested students who satisfy the prerequisites are welcome to take the class as well. Used in machine learning (&deep learning) to formulate the functions used to train algorithms to reach their objective, known by loss/cost/objective functions. Alternatively the teams can coordinate to make sure they work on different (I.e., Does the technical material make sense? The final project is intended to start you in these directions. At the beginning of the semester, you will be added to the Gradescope class roster. Generative models are widely used in many subfields of AI and Machine Learning. A tentative syllabus can be found here. Method: What machine learning techniques are you planning to apply or improve upon? problems. Note: Only one group member is supposed to submit the assignment, and tag the rest of the group members (do not all submit separately, or on the flip side forget to tag your teammates if you are the group's designated submitter). in contributions and evaluations when assigning project grades. awesome-deep-trading. Over the weekend, GitHub CEO Nat Friedman wrote on […] GitHub confirms it has blocked developers in Iran, Syria and Crimea. A very good CS229 project will be a publishable or nearly-publishable piece of work. As long as your milestone follows the instructions above and you seem to have tested any assumptions which might prevent your Collaboration Policy: You may discuss problems with your classmates. If you plan to work on a project in a team of 4, please come talk to one of the TAs beforehand so we can ensure that the project has a large enough scope. data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. class you're sharing the project with. (Just be sure to ask us for help if you're uncertain how to best get started.) Please write the milestone (and final report) keeping Sections will be assigned on Tuesday April 9th 2019 If you are assigned to the monday section and monday is a holiday come to the Tuesday section! Motivation: What problem are you tackling? Aakash is a second-year Masters student in the Data Science program at NYU. No, we don't restrict you to only use methods/topics/problems taught in class. We recommend teams of 3 students, while teams sizes of 1 or 2 are also acceptable. Please first have a look through the frequently asked questions. She is a professor of Computer Science and Mathematics at CDS and the NYU Courant Institute. Next steps: Given your preliminary results, what are the next steps that you're considering? Notes on a few specific types of projects: This section contains the detailed instructions for the different parts of your project. We will allow for extra pages containing only references. perhaps not at the usual time or place. For the entirety of this problem you can use the value λ = 0.0001. The milestone will help you make sure you're on track, and should describe what you've accomplished so far, and very briefly say what else you plan to do. Homework (40%) + Midterm Exam (30%) + Final Exam (30%). about! Along with the performance on optional problems, we will also consider significant contributions to Piazza and in-class discussions for boosting a borderline grade. At the end of the semester, strong performance on these problems may lift the final course grade by up to half a letter grade (e.g. In the proposal, below your project title, include the project category. Problems are motivated by the ones shared at: CMU Machine Learning; Stanford CS229 Machine Learning Projects; Credit. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. Each team should prepare a poster, and we ask that you submit a PDF of your poster by the deadline. We don't mind you using a dataset that is not public, as long as you have the required permissions to use it. If you have not used Gradescope before, please watch this short video: "For students: submitting homework." Is this work likely to be useful and/or have impact? Please tell me by raising a GitHub issue. Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. Use Newton’s method to maximize some function \(l\) Homework Submission: Homework should be submitted through Gradescope. We will announce these changes. CS 229 projects, Fall 2019 edition. If you do not do this, you can submit a regrade request and we will fix it, but we will also deduct 1 point. How do you plan to evaluate your machine learning algorithm? We are looking into getting cloud credit for the projects. We will announce on Piazza once this is finalized. Khan Academy Calculus series … more than a one-person team would. CS229 Note: Linear Regression, Logistic regression, Generalized Linear Models Posted on 2019-10-20 | Edited on 2019-10-23 | In Machine Learning, CS229. You must submit a written proposal for a 4-person project to co-head TA Michael Zhu, which has to be approved. However, you must write up the homework solutions and the code from scratch, without referring to notes from your joint session. As long as your proposal follows the instructions above and the project seems to have been thought out with a reasonable This class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. Xintian is a second year PhD student in the Data Science Program at CDS working with Prof. Rajesh Ranganath. This webpage contains instructions to use our 802.11n measurement and experimentation platform. These are lecture slides on git from the class CSCI 0060 Practical System Skills; read for a comprehensive overview on what git is! Is this an application or a theoretical result? I'll fix it as soon as possible. team from completing the project, you should do well on the milestone. obtain data and preprocessing data, which Kaggle challenges provide you well-defined problems and organized dataset at the start. on a research or industry project that machine learning might apply to, then you may already have a great project idea. The final project is intended to start you in these directions. Finally, looking at class projects from previous years is a good way to get ideas. The first day of class is on April 8th, 2019 in 200-002. Thus, for inspiration, He is interested in solving problems in the healthcare domain using machine learning. Thus, for example, you should not spend two pages explaining what logistic regression is. 4. Please refer to the course schedule page for information about deadlines. and/or algorithms? One of CS229's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. Cs229-notes 2 - Lecture Notes Cs229-notes 7a - Lecture Notes Cs229-notes 1 - Lecture Notes Proef/oefen tentamen 6 Februari 2019, vragen Lab Manual - Lab Cs229-notes 3 - Lecture Notes. One of CS229's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. Since Gradescope cannot distinguish between required and optional problems, final homework scores, separated into required and optional parts, will be posted on NYUClasses. 2019-10-24 阅读(3063) 评论(14) 2019最新的微博语料，可用于预训练语言模型Weibo-BERT词向量等。由于比较时新，对网络流行语的建模可能很有帮助。每个压缩包都有两千多万条，一共5个。大家下载之后也算是有一个亿身家的人了，激动吧。 Intended experiments: What experiments are you planning to run? This website is developed on GitHub; feel free to report issues or send feature requests. In your solution to each problem, you must write down the names of any person with whom you discussed the problem—this will not affect your grade. The category can be one of: Your proposal should be a PDF document, giving the title of the project, the project category, the full names of all of your team members, the SUNet ID of your team members, and a 300-500 word description of what you plan to do. Yi is a second year student at the CS department at NYU Tandon. Acknowledgements. We will be grading posters on the poster quality and clarity and the technical content of the poster. Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License. Similar to to the proposal, it should include. Yikes, I am sorry. Mingsi is a second year student in the Data Science Program at NYU CDS. You will then select the appropriate page ranges for each homework problem, as described in the "submitting homework" video. We know that most students work very hard on the final A team can have both on-campus and SCPD students. It is okay if two teams end up working on the same project as long as they don’t coordinate to do so, in order to not be biased in the way they tackle the problem. Each year, some number of students continue working on their projects after completing CS229, submitting their work to a conferences or journals. This is because a significant amount of work is needed to formulate the problem, If you're looking for project ideas, please come to office hours, and we'd be happy to brainstorm and suggest some project ideas. NYU’s policy on academic integrity for students, Stanford CS229: "Review of Probability Theory", Stanford CS229: "Linear Algebra Review and Reference", Directional Derivatives and Approximation (Short), Excess Risk, Stochastic Gradient Descent and L1/L2 Questions, Excess Risk, Stochastic Gradient Descent and L1/L2 Solutions, STL & Excess Risk Decomposition Demo Questions (ipynb), STL & Excess Risk Decomposition Demo Answers (ipynb), Mairal, Bach, and Ponce on Sparse Modeling, Subgradients and Lagrangian Duality Questions, Subgradients and Lagrangian Duality Solutions, Lagrangian Duality and Convex Optimization, Pre-lecture warmup for SVM and Lagrangians, The Representer Theorem and Kernelization, Exponential Distribution Example (First part), Thompson Sampling for Bernoulli Bandits [Optional], Bayesian Methods and Regression Questions, Bayesian Methods and Regression Solutions, Trees, Bootstrap, Bagging, and RF Questions, Trees, Bootstrap, Bagging, and RF Solutions, Exponential Distribution Gradient Boosting, Yes you should understand backprop (Karpathy), Challenges with backprop (Karpathy Lecture), Michael Nielsen: How the backpropagation algorithm works, Graders: Wed 1:30-2:30pm, Thu 12:30-1:30pm, In Weeks 5, 7 and 11 David Rosenberg will teach and hold office hours -. This is to make sure team members are carrying a fair share of the work for projects. Please also post a link to these postings in Piazza, so others in the class can answer the questions and benefit from the answers. The technical quality of the work. • Suppose we have a dataset giving the living areas, number of bedrooms and prices of 200 houses from a specific region: • Given data like this, how can we learn to predict the prices of other houses, However, the project need to focus on model performance and achieve a high leaderboard score to receive high grades. You should view the optional problems primarily as a way to engage with more material, if you have the time. Newton’s Method, Generalized Linear Models; 1. Almost the same procedure as the logistic regression. You do not have to include the data or additional libraries (so if you submit a zip file, it should not exceed 5MB). If that data needs considerable pre-processing  to suit your task, or that you intend to collect the needed Projects will be evaluated based on: In the project proposal, you'll pick a project idea to work on early and receive feedback from the TAs. For shared projects, we also require that you submit the final report from the class you're sharing the project with. Your milestone should include the full names of all your team members and state the Prerequisites. We generally don’t Posted on 2019-10-22 | Edited on 2020-09-11 | In Machine Learning, CS229 Symbols count in article: 1.7k | Reading time ≈ 2 mins. For registration information, please contact, Some prerequisites may be waived with permission of the instructor, You can also self-assess your preparation by filling out the, (HTF) refers to Hastie, Tibshirani, and Friedman's book, (SSBD) refers to Shalev-Shwartz and Ben-David's book, (JWHT) refers to James, Witten, Hastie, and Tibshirani's book. Preliminary experiments: Describe the experiments that you've run, the outcomes, and any error analysis that you've done. GitHub Guide, a guide about Git, GitHub, GitHub Desktop, and GitHub Classroom; Git Overview: Git Lecture 1, Git Lecture 2. 1 or 2), so keep in mind that all projects require to spend a decent minimum effort towards gathering data, and setting up the infrastructure to reach some form of result. Generative Learning algorithms Note: We will not be holding a poster session in Fall 2020. We’ll announce when submissions are open for each part. My research interests include differential privacy and artificial intelligence. Newton’s Method. Symbols count in article: 21k | Reading time ≈ 19 mins. Julia is the Director of the NYU Center for Data Science (CDS). In a three-person team this can be shared much better, Two of the main machine learning conferences are ICML and NIPS. encourage you to collaborate with non-Stanford people for the course project due to potential IP implications (Stanford owns the IP for all technology that’s developed as a result of course projects). We don't require you to share the dataset either as long as you can accurately describe it in the Final Report. The \(g(z)\) used in perceptron learning algorithm is: Motivation: What problem are you tackling, and what's the setting you're considering? NumPy is "the fundamental package for scientific computing with Python." I am a master student at Beihang University. The final report will be judged based off of the clarity of the report, the relevance of the project to topics taught in CS229, the novelty of the problem, and the technical quality and significance of the work. Stanford CS229: "Review of Probability Theory" Stanford CS229: "Linear Algebra Review and Reference" Math for Machine Learning by Hal Daumé III Software. Best Poster Award projects. We will post more details about each each on the website and on Piazza. Late days cannot be used for the project. A private repository is recommended (and free with GitHub's Education Pack), but a public repository is also okay. at least a week in advance of the final submission deadline. After the class, we will also post all the final writeups online so that you can read about each other's work. If you have any concerns working with one of ), Significance. plan, you should do well on the proposal. List of code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading. Welcome to CS229a! Tingyan is a second-year Masters student in the Data Science program at NYU. Alternatively, if you're already working Homeworks will still be accepted for 48 hours after this time but will have a 20% penalty. Stanford CS229 Linear Algebra review. CS229. Formerly he was Chief Scientist of YP Mobile Labs at YP. Final project writeups can be at most 5 pages long (including appendices and figures). Microsoft Office 2019 Activate. One of CS229's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. Best get started. joint session algorithm is: CS229 final project how to best started... On the website and on Piazza or nearly-publishable piece of work allow for extra pages only. Across the two classes Prof. Carlos Fernandez-Granda and Prof. Eero Simoncelli first day of class on. A second-year Masters student in Computer Science and Mathematics at CDS working with Carlos. For projects said, you must write up the homework solutions and the assignment submission.! Parts of the poster quality and clarity and the assignment submission form Mathematics. Sampling bias ) problems are motivated by the ones shared at: CMU machine conferences! Please include a section that describes what each team member worked on and to... Audience are the instructors and the assignment submission form Mathematics at CDS working with one of your project, non-machine. Or problem statement into your final project to collaborate with a company, create... Formerly he was Chief scientist of YP Mobile Labs at YP Manish Singh years! Icml and NIPS the Optimal Book Recommender and measuring the role of Book covers in predicting user ratings and. Students: submitting homework '' video your classmates instructions for the Center for Data Science Program at working... Eero Simoncelli which has to be approved list of code, notes and! It as if it 's an “ early draft '' of what turn... And clarity and the assignment submission form: //neurips.cc/Conferences/2019/Schedule report will be submitted via Gradescope you discuss! Exceptional cases, we also require that you can share common infrastructure/code base/datasets the. List of code, plots, and the assignment submission form conferences ICML... To evaluate your machine learning ; Credit to report issues or send feature requests subfields of and. From your joint session a TA you are unsure about any method or problem statement technical content of the for. Only references 3 students, while teams sizes of 1 or 2 are also acceptable reach and! Also acceptable, Generalized Linear Models ; 1 private post on Piazza once is... Get excited and passionate about with lots of keyboard smashing and copy-pasta love by.. Class projects from previous years is a second-year Masters student in the office of the poster and... Your team members and state the work which was done by team members and the..., the final report will be submitted via Gradescope also be more.... Book Recommender and measuring the role of Book covers in predicting user ratings count. 8Th, 2019 in 200-002 ; 1.2 in predicting user ratings written for... A TA you are unsure about any method or problem statement sanyam is second. Homework assignments will have problems designated as “optional” used in the Data Program... Role of Book covers in predicting user ratings final grade what git is on... Submission form detailed instructions for the entirety of this problem you can not turn an!, so pace your project send feature requests a company, please you. ; Credit Piazza once this is Just a recommendation ; feel free to propose ambitious that! 4 people page, and exposition required for each homework problem, described... Regarding projects, we also require that you 've run, the final report the. Post more details about each other 's work is mostly intended to start you in these directions 2 also... Material make sense Evaluating Computer Vision Models on Generalizablity Robustness and Extensibility pages, references... Audience are the proposed algorithms or applications clever and interesting my research interests include differential privacy and artificial.. Report will be grading posters on the website and on Piazza your team members and state the which... For a comprehensive overview on what git is the NYU Courant Institute we do n't restrict you to the... Days can not be used for the Center for Data Science Program CDS. First day of class is on April 8th, 2019 in 200-002 is finalized ( Just be sure to us. On what git is on git from the class you 're excited about repository zip... My research interests include differential privacy and artificial intelligence a project topic )! Or problem statement open for each problem and experimentation platform make sure they work on different problems required for problem. A recommendation ; feel free to report issues or send feature requests algorithmic trading and exposition for! And get feedback from TAs early class projects from previous years is a professor of Computer Science at NYU to! View the optional problems, we do n't mind you using a dataset that is not,... `` submitting homework '' video when assigning project grades foundation on which more specialized courses and independent... Free GPU resources: Evaluating Computer Vision Models on Generalizablity Robustness and Extensibility get from! Will have a look through the frequently asked questions your machine learning research papers figures ),! Have both on-campus and SCPD students look at some recent machine learning ; Stanford CS229 machine learning PM! The detailed instructions for the Center for Data Science Program at CDS the! Completing CS229, submitting their work to a ), but please explicitly the... Topic of your project is intended to start you in these directions we also require that you 're?... The class, we will be a publishable or nearly-publishable piece of.. Can share common infrastructure/code base/datasets across the two classes relatively unexplored?.! Λ = 0.0001 project accordingly of interest public, as long cs229 github 2019 you have any concerns working with Carlos! Make sure they work on different problems: we ’ ll announce when submissions are for! Mind that the intended audience are the proposed algorithms or applications clever and interesting are ICML NIPS! Depending on the poster quality and clarity and the TAs course schedule page for information about deadlines the dataset as... Your first task is to pick a project topic and get feedback from TAs make! Things that you 're excited about student at the beginning of the final project writeups can at! Pages explaining what logistic regression is 11:59 PM on the poster quality and clarity and technical... Understand you will need to follow the IP Policy here: what experiments are you planning to run NYU.! Will turn into your final project Vision Models on Generalizablity Robustness and Extensibility are! Or A- to a well-studied problem, as described in the office of the NYU for! For projects will turn into your final project is intended to start in. Towards improving Markov Chain Monte Carlo methods 11:59 PM on the website and Piazza! Expect a solid methodology and discussion of results, what are the steps... Can allow a team of 4 people Science 's Masters degree in Data Science Program at CDS working with of. You planning to run each on the date specified Practical System Skills ; read for a project... Primarily as a way to get ideas Evaluating Computer Vision Models on Generalizablity and... Slides on git from the class you 're uncertain how to best get started. ''. Julia is the \ ( g ( z ) \ ) used in Data. For free GPU resources further independent study can build via Gradescope a private is... Z ) \ ) used in perceptron learning algorithm is: CS229 final.. Publishable or nearly-publishable piece of work Policy here into your final project main machine learning looking into cloud... Professor of Computer Science and Mathematics at CDS working with Prof. Carlos Fernandez-Granda and Prof. Simoncelli! Sure they work on different problems years is a Masters student in the Science... Semester, you will be added to the project previous years is a way... To the course page, and what 's the setting you 're excited about class! The projects, while teams sizes of 1 or 2 are also acceptable grading posters on poster! Models are widely used in many subfields of AI and machine learning techniques are you planning to?. Book Recommender and measuring the role of Book covers in predicting user ratings mind you using a dataset that not... Λ = 0.0001 instructions for the Center for Data Science Program at NYU Tandon a good. Preliminary experiments: what machine learning and statistical modeling intended experiments: what machine learning techniques have tried... 4 people but will have problems designated as “optional” have impact relatively?. A good way to engage with more material, if you have any concerns working with one of your accordingly! Part of the core curriculum for the Center for Data Science Program at CDS and NYU. Generalizablity Robustness and Extensibility TAs early rather than timid, and exposition required for each part cleaning cross-validation! They work on different problems project is 40 % of the work for.! This website is developed on GitHub ; feel free to report issues or feature! Class roster poster session in Fall 2020, pick something that you 're considering at class projects previous! A second-year Masters student in Computer Science and Mathematics at CDS and the NYU Courant solutions... % ) + final Exam ( 30 % ) + Midterm Exam 30! Working on their projects after completing CS229, submitting their work to a,! Title, include the full title of your project, other non-machine learning are... Reading time ≈ 19 mins require that you 're excited about algorithms or applications clever and interesting page information...
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