Final Projects- (Machine Learning Spring 2003)

  • The project accounts for 40% of your final grade marks
  • The point allocation in the projects will be as follows:
    • 40% : Relevance, motivation & formulation of the problem
    • 40% : Methods and Results
    • 20% : Presentation (how well you get ideas across in the time allocated to you).
  • Your team will be asked to make a 15 min presentation using ppt. or conventional slides
  • You should summarize the most important results in a one page summary to be handed over to the evaluators at the time of presentation
  • You will be evaluated by 3 independent evaluators present at your presentation
  • The guidelines for the project are here.

Day 1 Presentations (April 29, 2003) 

Team No. Team Members
Project Title
1.  (18) Maurya Shah, Changki Min, Soonil Kwon Multi-K Nearest Neighbor Approach To Speaker Identification
2.  (03) Jeff Begley, Hal Daume III Machine Learning of Primate V1 Neuron Responses to Natural Scenes
3.  (04) Dasarathi Sampath, Snehal Thakkar Is The Speaker Done Yet? Identifying Turn Taking in Human-Agent Conversations
4.  (08) Jong H. Kim, Ming Li, Shipra Mehta Characterizing protein interaction networks and predicting new interactions
5.  (19) Junkwan Lee Awkward bass player: Root decision from Bayesian inference
6.  (15) Stefan Hrabar, Maxim Batalin Feature Recognition in Omni-camera Warped Images
7.  (13) Donghui Feng, Namhee kwon, Quamrul Hasan Tipu Statistical MT with Bilingual Morphology
8.  (10) Denis Wolf, Sameera Poduri SLAM Using Mobile Robots
9.  (02) Zhigang Deng, Hongwei Wu Dimensionality Reduction with Missing Data: A Comparison of 3 Methods
10. (07) Shravan Heroor Genetic Algorithm for Self-Learning Bipedal Robot Motion

Day 2 Presentations (May 06, 2003)

Team No. Team Members
Project Title
1.  (16) Dylan Shell, Gabe Sibley Learning adaptive gaits for the JPL Spider-bot
2.  (14) Li Wei, Hu Haiyan Multi-objective genetic algorithm for gearbox design
3.  (11) Kiyoung Yang, Hyunjin Yoon Hand Motion Recognition with Support Vector Machine - A Kernel Based Approach
4.  (06) Apirak Hoonlor, Worawit Panpanyatep Discovery of Linear Order in an Ancient Script
5.  (09) Nathan Mundhenk, Srihari Vasudevan, Vidhya Navalpakkam Object Categorization
6.  (05) Tzu-Chien Lai, Shou-de Lin Web Content Mining and Classification
7.  (12) Larry Kite, Xiangyu Tang, Shuang Wu Evaluation of machine learning methods for gaze tracking
8.  (01) Bingwen Lu, Hyunju Lee Application of Support Vector Machine in Protein Identification via Tandem Mass Spectrometry
9.  (17) Michael Brasser, Arda Celebi, Nick Mote Probabilistic Latent Semantic Analysis and it's application to Machine Translation