17 skills found
micans / MclMCL, the Markov Cluster algorithm, also known as Markov Clustering, is a method and program for clustering weighted or simple networks, a.k.a. graphs.
koteth / Python Mclmarkov cluster algorithm - python
abietti / Online HmmOnline EM algorithms for hidden Markov and semi-Markov models + applications to audio segmentation and clustering
svalkiers / ClusTCRCDR3 clustering module providing a new method for fast and accurate clustering of large data sets of CDR3 amino acid sequences, and offering functionalities for downstream analysis of clustering results.
joandre / MCL SparkAn implementation of Markov Clustering algorithm for Spark in Scala
tahanakabi / Optimal Price Based Control Of Heterogeneous Thermostatically Controlled Loads Under Uncertainty Usiwe consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
bottero / IMCMCrunIMCMCrun is used to perform a seismic inversion using a fast eikonal solver and interactive markov chains algorithm. TODO add reference to the article when it is done. Author : Alexis Bottero (alexis Dot bottero At gmail Dot com). Feel free to contact me for any questions. Source code (c/c++/fortran) can be found in directory src. This program include wavelet library : Wavelib. This program can be run in parallel on a CPU cluster. Directory utils contains some useful python script to watch the results of an inversion.
ychennay / RJMCMCDatasets and code for CS226 (Machine Learning) Research Project (December 2016). The endproduct is a reversible jump Markov Chain Monte Carlo algorithm to define the appropriate clusters of genetic ancestry with a sample of human genomes.
jaroslav-kuchar / MarkovClusteringMarkov clustering algorithm
aaadriano / 672 Statistical LearningKernel Principal Component Analysis, Spectral Clustering, Gaussian Processes, RKHS of vector-valued functions, RKHS embedding of the realization of random variables, Tests of independence and conditional independence, Bayesian networks, k-means, mixture models, the expectation maximization algorithm, Markov random fields, Gibbs distributions, belief propagation algorithms, variational inference, Markov chain Monte Carlo.
oflisback / Markov ClusteringMarkov clustering algorithm implementation in JavaScript
jamesneve / Go Markov ClusterBasic implementation of the Markov Clustering algorithm for go
YasPHP / UofT LearnAIAn Introductory ML Educational Program hosted by the UofT AI Society. Topics include, Data Manipulation, Classification & Regression, Neural Networks, Computer Vision (CNNs), Natural Language Processing (RNNs), Reinforcement Learning (RL), Markov Decision Process (MDP), Genetic Algorithms, Decision Trees, K-means Clustering, Minimax, Hidden Markov Model.
cytoscape / Cytoscape.js Markov ClusterA Cytoscape.js extension for the Markov cluster algorithm
AndrasHartmann / MMCLMATLAB implementation of the Markov CLustering (MCL) algorithm
Rinoahu / MCL Litea memory-efficient implementation of Markov Clustering Algorithm for large-scale of networks
