Pinned Repositories
Automatic-Speech-recognition-ASR-
Feature vector for Automatic Speech recognition :Mel Frequency Cepstral Coefficient (MFCC)
Decision-Tree-in-Python-for-Continuous-Attributes
This code constructs a Decision Tree for a dataset with continuous Attributes. Each training instance has 16 numeric attributes (features) and a classification label, all separated by commas. In deciding which attribute to test at any point, the information gain metric is used. The node test threshold for each potential attribute is set using this same metric i.e. at each point, all the values that exist for a particular attribute in the remaining instances are ordered, and threshold values that are (half way) between successive attribute values are used to find the Information Gain. The threshold value that gives the highest information gain is used. The same attribute can be tested again later in the tree (with a different threshold).
ensemble-of-sarima-random-forests-and-gradient-boosting-trees
In this Project I use the Kaggle Bike sharing dataset to predict the sales of bike given a Multivariate Time series. I model the multivariate data using ensemble of Random Forests and Gradient Boosted trees. After that the residuals of the model are fit with an ARMA/ARIMA/SARIMA model and later forecasted. The residuals are later added back to the predicted values
genetic-algorithm-for-cnn
This project tunes a Convolutional Neural Network using a genetic algorithm for Image Classification.
graph-based-semi-supervised-learning
This project explores the different techniques (both scalable and non scalable) for Graph based semi supervised learning. Recent techniques such as ITML and LMNN along with a few others are empirically evaluated on the 20 newsgroups dataset.
Hidden-Markov-Model
This Code Implements the Hidden Markov Model (Monitoring and the Viterbi Algorithm) in Python on a Time series Data.
Machine-learning-algorithms
SMO algorithim for training support vector machines,Independent component analysis,Principal Component analysis
Tic-Tac-Toe-Using-Alpha-Beta-Minimax-Search
This code demonstrates the use of Alpha Beta Pruning for Game playing. Since, Tic Tac Toe has a depth of 9 , I use a heuristic function that evaluates the Board State after searching through a depth of 3. The heuristic function calculates the expected score of winning for the PC given the board state.
tsp-using-simulated-annealing-c-
This code solves the Travelling Salesman Problem using simulated annealing in C++.
Viterbi-algorithm
The Viterbi algorithm is tagging algorithm based on TRIGRAM HIDDEN MARKOV MODELS (TRIGRAM HMMS)
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