CPSC 633-600 Read-Only Bulletin Board

Last modified: 1/17/11, 10:28AM (Mon)

This page will have all relevant email transactions with the students regarding the AI course, so that everyone has equal information regarding the course material.

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Article List

Date: 03/31/11 Title: Project teams and topics
Date: 03/24/11 Title: Xbraitenberg package debugging
Date: 03/23/11 Title: Project Instructions
Date: 03/03/11 Title: Generating random events


Date: 03/31/11 Title: Project teams and topics

Project teams and topics

These topics can change. Jialong Zhang and Wei Li URL maliciousness detection: Investigating relevant features Raymond Lin and Francisco Vides Coevolution and predator and prey using NEAT Gopal Krishna Kedia and Saurabh Mahta Document classification: Exploiting session based implicit links Sumanth Reddy Kommidi and Santosh Kumar Eppalapally Interest classification and recommendation system in Twitter Ben Fine and Joshua Pschel Distinguishing Between Finger-Sketched Shapes versus Text Using Higher-Order Entropy Hutson Betts and Spencer Huang Determining most informative features and minimum amount of information neeeded from movie reviews Nakul Anil Ingley and Haokai Lu Active vision with neuroevolution Sampath Jayarathna and Radhika Kashyap Community Recommender Service (CRS) Angela Faragasso and Flavia Schiavo Character recognition Santosh Kumar Vanaparthy Maximizing information transmission over finite state erasure channel with memory Mohammadali Tarrahi and Amin Rasekh Efficient probability density function estimator for Bayesian optimal classification: Application to Abalone benchmark data set Subhadeep Chakraborty and Varun Raj Tag recommendation for text documents Shuhan Xu and Mingqu Yue Genetic algorithms for spelling correction Longfei Zhang and Shuai Ye Detecting beauty in color combination Jonathan Hall and Hone-hoe Kim Automatic title generation from text documents of different type
Date: 03/24/11 Title: Xbraitenberg package debugging
To fix compilation problem in the xbraitenberg package, change the beginning part in vector.hh as follows: #ifndef VECTOR_HH #define VECTOR_HH #include <assert.h> #include <stdlib.h> #ifdef HAVE_NEW_H #include <new.h> #else //static inline void *operator new(size_t, void *v) { return v; } #endif #include <new> ... Once you get it compiled, try ./xbraitenberg -l l2a l2b v2b v2b v2b v2b v2b
Date: 03/23/11 Title: Project Instructions


  1. Backpropagation: backprop-1.6.tar.gz (C++ code -- unix)
  2. Neuroevolution: ga.m (Octave code)
  3. SIDA: sida-nat.tar.gz (Octave code)
Final miniproject details 1. Pick a code-base - YC's backpropagation code (in c++) - YC's neuroevolution code (in octave) - YC's SIDA code for sensorimotor semantics (in octave) - Your own code - Reinforcement learning code (from homework) - Other - A third-party open source: NEAT, etc. 2. Formulate your research problem - Pick task - Pick a task related to your own research area. - Locate data set (if needed): see, e.g., UCI ML repository - Design experiments - This is perhaps the most important! - Just comparing existing learning algorithms on a data set is not meaningful. - Design experiments to discover new insights about data. Example: - Discover new structure in data (using dim reduction, etc.) - Discover dependencies among features - Novel forms of data (connectivity, etc.) - etc. - Critically evaluate commonly accepted or implicit assumptions. Example: document classification 1. single document, natural language processing needed. 2. single document, counting word cooccurrence enough. 3. need to consider not just single document but documents linked to it. 4. etc. - Derive new learning algorithms Example: - semisupervised learning - active learning - ... 3. Proposal - Team members: max 2 per team. Individual projects are allowed only under special circumstances. - What is the research problem? - Why is it important/interesting? - What are other people's approaches? - What are other people's assumptions? (optional) - What are the limitations of those approaches? - What are the problems with those assumptions? (optional) - What is your approach? - How will you relax the assumptions? (optional) - What experiments will you do? - What data set will you use (if applicable) - What code base will you use - What kind of experiments will you do with the data/code? - What are the expected results? - What do you expect to achieve by relaxing the assumptions? (optional) - Submit by 3/29 3/31 (Thu), in class. Hardcopy. 4. Final report - 4-5 page, single space report. One column or double column. - Conference style (e.g., IEEE conference style).
Date: 03/03/11 Title: Generating random events
How to generate probabilistic events:

i. Given choices X and probability p(X).
ii. Choose from choice
	c = ceil(rand*cardinality(X))
iii. Generate random number between 0 and 1.
	r = rand;
iv. if (r<p(X=c))
	accept choice c
	reject and go to step ii.

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