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![Propuesta de Proyecto de Inteligencia Artificial](https://writelatex.s3.amazonaws.com/published_ver/2811.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=b366dbbe770ccb2b92b360df4270be0734c07b66c0682e3b1805f90ae679add3)
Propuesta de Proyecto de Inteligencia Artificial
tres e n raya
Elvis Adean - Kevin Choez - John Garcia
![Cycle Detection Algorithm: the Hare and the Tortoise](https://writelatex.s3.amazonaws.com/published_ver/937.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=59307ba82fffb1225261e5c732ac3fa913e01d3d56b90c50c2e8dc41ffa89b4a)
Cycle Detection Algorithm: the Hare and the Tortoise
Explain how the Floyd's cycle detection algorithm works.
Frank the Bunny
![Certeza da Salvação](https://writelatex.s3.amazonaws.com/published_ver/2312.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=20038d56770d8a679326e65385fc11559ccb872936618b99c9fd9ac347f83400)
Certeza da Salvação
Estudo baseado no Sermão do Rev. Augustus Nicodemos Lopes
http://www.monergismo.com/textos/certeza/certeza_augustus.htm
Egmon Pereira
![Industrial I/O](https://writelatex.s3.amazonaws.com/published_ver/2397.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=99be6f14dc117e02df42f6b3a96d30498ad99b37935d64d4e7f98e6e7da1d07e)
Industrial I/O
Created with the Beamer Presentation LaTeX Template Version 1.0 (10/11/12) from
LaTeXTemplates.com
Daniel Baluta
![Autonomous robot navigation in mixed environment](https://writelatex.s3.amazonaws.com/published_ver/2391.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=4ce3ddd09c1333b8f75ae999afac8077a127fdf42863a76927c0198df1e14720)
Autonomous robot navigation in mixed environment
Created from portrait poster template
OviRush
![Nourchene Belouehm's CV (Computer Networks Engineer)](https://writelatex.s3.amazonaws.com/published_ver/2841.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=07df2a3801ff612fe45294a38854a2cf458265d913d3a650149af96d26e405ea)
Nourchene Belouehm's CV (Computer Networks Engineer)
Created using the ModernCV template
nourchene
![MOOC Dropout Prediction with Model Stacking](https://writelatex.s3.amazonaws.com/published_ver/7620.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121756Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=318b9a627f98371a8eb86aeb003a9a0dfb7064e361596ba835d8641993813697)
MOOC Dropout Prediction with Model Stacking
We are asked to predict the probability of the event that a student will drop out a course. We firstly extracted many features from the huge dataset. Then we used ensemble learning machine and model stacking technique to get the final result, which ranked the 1st in 68 teams.
Qi Zhao
![Hypothetical uniform quantizer](https://writelatex.s3.amazonaws.com/published_ver/10810.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121757Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=45d99ad1171865d4276baef290200205037d31ae1c537326d084f0c41233cc3c)
Hypothetical uniform quantizer
Hypothetical uniform quantizer standalone class figure.
Lucas dos Santos Costa
![Automatic Structural Segmentation of Music: Insightfully clustering the beats in a given piece of music to reflect it's musical structure](https://writelatex.s3.amazonaws.com/published_ver/3920.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240701T121757Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240701/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=fd19c7feda086325d5591c00359e448d660b01dd20ce78a1326e1b02c8562b38)
Automatic Structural Segmentation of Music: Insightfully clustering the beats in a given piece of music to reflect it's musical structure
This Project posits elementary analogies of existing Probabilistic and Machine Learning models that have been used to find solutions to the problem of the Structural Segmentation of Musical audio. I have tried to use the idea that the chord of a given beat or frame of a song is an analogous representation of the states generated by trained Hidden Markov Models in generating feature vectors for the aforementioned problem; and that the knowledge of the temporal boundaries within which, a group of frames lie, can be used as constraints in creating the feature vectors that are eventually clustered to identify the pattern in which the various segments of a song repeat.
Lyndon Quadros