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Innovative Machine Learning Projects: Dive into Real Data Analysis with Cutting-Edge Techniques

Embark on an exciting machine learning project where you explore unique techniques with a focus on raw data analysis. Dive deep into the intricacies of various algorithms while conducting insightful reviews and surveys. Be prepared for a comprehensive progress report and a final presentation along with a detailed report. Choose a topic that challenges your analytical skills and promises a real-world impact. Stay updated on project deadlines and requirements to ensure a successful outcome.

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Innovative Machine Learning Projects: Dive into Real Data Analysis with Cutting-Edge Techniques

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  1. 2009 ML Project: Goal: Do some real machine learning… • A project you are interested in works better • Data is often the hard part (get it in plenty of time) • Usually involves additional reading • Using software packages OK, particularly comparing algorithms is fine • Insightful survey/review paper OK (may involve a longer talk) • Theory problem OK, but high-risk

  2. Requirements • Project proposals (due February 19) • 1 single-spaced page • Problem, data, and planned approach • Progress report (due March 24) • One to two pages • Brief, self-contained description of progress, • problems, and expected scope of final project • Short Presentation (April 7 or 9) • Final report (due April 11) • Body (~12 to 24 pages of text, 12 pt font) with references and citations. Can have appendices. • Should have good introduction/abstract (like a conference paper) and discuss related work

  3. Example Project Themes • Exploring techniques for file pre-fetching • Finding scene lighting from face images • Predicting senator’s votes based on campaign contributions • Customer and product clustering for recommender systems • Comparing Naïve Bayes and Neural nets for text classification • Adaptation in a virtual animal world using reinforcement learning • Road extraction from Aerial images • Predicting success of major league baseball teams • Exploring methods for handwritten digit recognition • Differences between ML and MAP estimators • Using Multi-dimensional Scaling for Dataset Visualization • A belief network tool predicting the presence and onset of a particular disease. • Predicting the future success/failure of graduate students

  4. Other Comments Individual Project • We have 36 students and we should have 36 different topics • You can not use things you did in the past PhD work as your project; however, it is perfectly alright to conduct a project that adds something to past work to conduct as part of your dissertation, MS thesis research. • Reference all sources you used in your final report including your own publications (not doing so is cheating!!!) • Some topics are too general to be completed in 6 weeks • Evaluating to which the techniques you developed work for the problem at hand is very critical for the success of the project. • Just using a piece of machine learning software might not be a good idea; comparing different approaches, on the other hand, is okay. • Where does the sudden popularity of hidden Markov models originate from? • Some project are too specific and it is not explained how the project fits into the general picture. • There has to be some learning in your project!! • Search, unless involves learning from past experience, is not a good topic.

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