10 Algorithms Machine Learning Engineers Need to Know
Want to know the most commonly used algorithms which can be applied to any data issue? Here's the list.
Looking for someone familiar with the below topics: 1. Artificial Intelligence and Agents - Artificial Intelligence - Agents and Envir...Search - Problem Definition and Formulation - Searching for Solutions - Uninformed Search Strategies - Informed (Heuristic) Search Strategies 3. Beyond Classical Search - Hill-Climbing Search - Simulated Annealing - Local Beam Search - Genetic Algorithms - AND-OR Search Trees - Adversarial Search - MiniMax Algorithm 4. Probabilistic Reasoning and Knowledge Representation - Probability Theory - Bayesian Networks - The Variable Elimination Algorithm - Rule-Based Methods for Uncertain Reasoning - Fuzzy Sets and Fuzzy Logic 5. Machine Learning - Forms of Learning - Supervised vs Unsupervised Learning - Decision Trees - Neural Networks - Support Vector...
I have MatLab code for a Naïve Bayes model which incorporates Markov transition probability into it. I have all the data the forecasted results and all the matlab code. However I don’t have MatLab and would like to see the model in R. All the information for this model can be found at The link for files is under the header “Additional Files”.
Must know at least 2 of the below techniques: • Bayesian Analysis • Topic Modelling • Data Ethics • FAT Analytics • Machine Learning • XAI Check the document attached and then Add you bid
a. Decision tree algorithm b. Naïve Bayesian classification algorithm c. A-priori algorithm d. K-means algorithm
...analysis. • Running machine learning tests. • Using results to improve models. • Training and retraining systems when needed. • Extending machine learning libraries. • Developing machine learning apps according to client requirements. Required Skills • Good knowledge of Python or R, SQL Advanced math and statistics skills, surrounding subjects such as linear algebra, calculus, and Bayesian statistics. • Advanced degree in computer science, math, statistics, or a related degree. • Master's degree in machine learning, neural networks, deep learning, or related fields. • Strong analytical, problem-solving, and teamwork skills. • Software engineering skills. • Experience in data science. • Experi...
...placebo or medicine). Unfortunately, the success of the treatment depends also on their mental health Z, which affects both their chances of survival and willingness to take the medicine. Assuming X ∈ {nothing, placebo, medicine} is the possible treatment, Y ∈ {survive, ¬survive} is the outcome, and Z ∈ {healthy, ¬healthy} is the mental condition: a) Represent the above situation with a Causal Bayesian Network (CBN) and its conditional probability tables (CPTs). b) Implement and document a Python program that reads the given dataset and automatically computes the intervention’s probability P(Y | do(X)) for all the possible values of X and Y. c) Modify the CBN to introduce another variable W ∈ {gene, ¬gene}, which indicates whether the patie...
Need someone who can assist me with these topics on particle filtering, Bayesian networks, kalman filtering etc.
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We are working in the healthcare sector generating synthetic medical images. We are working with Generative Adversarial Networks, Markov Chain Generators and maybe Bayesian Networks. We would require support in developing the models. Languages: R, Keras/ Tensorflow
Bayesian small project needed urgent
this project requires skills for working with JASP, R studio, and Bayesian statistics
this statistical project requires working with JASP and R programming - Bayesian stats
I need help to Write function to determine prime numbers in F#, Simulate different memory allocation strategies in C, Construct a Bayesian network, i will send more details in the chat.
I need a person who can help me in probabilistic modeling? it entails a Bayesian approach, Pareto distribution, decision theory, and graphical model
Hi Quant M., I have a bayesian project, and was looking to spend $60, would ou be able to support?
Using JAGS, implement a Bayesian version of the model in part a), i.e. a Bayesian Poisson GLM with log link function and the number of persons acting as the offset variable. Explain how did you construct your model. Use a Gaussian prior with mean 0 and variance 100 for the intercept, and independent Gaussian priors with mean 0 and variance 1 for the other 3 regression coefficients. Do 5000 burn-in iterations, and obtain 10000 samples from all regression coefficients from this model using coda.samples. More details to this will be shared with selective candidates only. please leave your bids so we can discuss this further. Thank you
There are 3 questions here and I would love a step by step to see how this works, with the rmatlab or r or Python code attached. possible for you to provide a step by step for each step that you take. This will help me a lot with learning the thought process and how I can improve. Should be submitted as word file with R/Matlab/Python code attached.
...were engineered using the y-variable. Criterion 3 – Model Types Model types are appropriate for the task at hand and come from scikit-learn (other packages and/or engines are not permitted). However, you may use statsmodels to evaluate your model statistics, as long as your final model is in scikit-learn. Permitted Model Types OLS Regression (standard linear regression) Lasso Regression Bayesian Automatic Relevance Determination (ARD) K-Nearest Neighbors Regression (KNN) Note that you are permitted to adjust the optional arguments of the permitted model types. Violation Penalty Final models that are not in the list of permitted model types will be discarded and the last appropriate model that ran in your code will be used as your final model. Final model poi...
Busco personas interesadas en encontrar soluciones a sesgos cognitivos y optimización personal. Por el momento me baso en la teoría de la información. key words matemathical thinking, racionalidade, solución de problemas con bayes, scientific thinking, bayes rule, bayesian network, entropy, mutual information, causal inference
Data scientists whose work load is more than acceptable levels may share data sets for naive bayesian model analysis and markovian chain analysis... Regression models that require transformation to raise the R and R square.
Hi, I want to understand about Bayesian linear regression with Python hands on. Someone who can explain about how the code works, if possible code if related to Market mix modelling will be helpful
Details listed in the image below, need the project done by 1:00 AM PKT 06/02/2022
...& Responsibilities – Proven experience as a Machine Learning Engineer or similar role Extensive data modeling and data architecture skills Programming experience in Python, R, or Java Background in machine learning frameworks such as TensorFlow or Keras Knowledge of Hadoop or other distributed computing systems Experience working in an Agile environment Advanced math skills (linear algebra, Bayesian statistics, group theory) Strong written and verbal communications Outstanding analytical and problem-solving skills Requirements Minimum 3 years of strong AI/ML algorithms working experience. Bachelor’s degree (BSc) in Computer Science, Mathematics, Statistics, or similar field; Master’s degree is a plus Benefits Cross-team work culture. Career D...
...ü Processing large amounts of data using basic SQL queries. ü Running statistical models or/and machine learning algorithms using R/ /SAS/Python (expert in two of them is a must) ü Hands On experience on Dataiku is a plus · Techniques typical used for analysis are : ü Simple and Multiple Linear Regression , ü Classification such as Logistic Regression and Discriminant Analysis ü Bayesian Regression using MCMC ü Resampling Methods such as Bootstrapping and Cross-Validation ü Dimension Reduction such as principal component regression and factor analysis · Client Communicate : ü Communicate results to stakeholders in requested format such as graphs, charts, and tables. ü Adapt statistical method...
I have a project working on a phylogenetic relationship of two species. I need to take the data into MrBayes for Bayesian analysis and also need to work on mPTP , multiple Poison Tree Processes. If you are familiar with this two technique, let me know. Thanks
I need help in Statistical analysis with Bayesian risk analysis, signal detection theory, gaussian and poisson processes. i will provide more detAILS IN THE CHAT.
Given some datasets, I need an expert in R to make some predictions using Bayesian networks, Naive classifiers, decision tree, random forests, support vector machine classification, Hierarchical clustering, PCA and K-Means clustering
The predictions include Bayesian networks and naive Bayes classifiers, decision tree and random forests classification algorithms, vector machine classification algorithm, Hierarchical clustering, PCA and K-Means clustering It is a very short project, that should not take more than a few hours to complete, hence my budget for this project is $30.
The predictions include Bayesian networks and naive Bayes classifiers, decision tree and random forests classification algorithms, vector machine classification algorithm, Hierarchical clustering, PCA and K-Means clustering
Problem Solving, route finding, Informed search and Heuristics, uniformed search, Adversarial Search, Fuzzy logic, Dynamic Bayesian Networks & Markov chains
...enhancement course and answered numerous questions. When some people have named their husband or partner while filling out the form, this is not the case for everyone. I need to figure out who the couples are. Several factors may be used to identify the spouses, including the number of the course they took, their age, income, the number of children they have, and so on. I'd like someone with Bayesian analysis knowledge to perform a regression, ideally with the brms package, to identify each pair and assign them an ID. 1. I want each respondent to be identified with their partner, however in certain situations, just one of a pair has responded, therefore they should not be "forced" to be in a relationship with someone with whom they have a low probability of ...
We have two deep learnings models that have already been created in PyTorch: 1. One multitasking feed forward model 2. One feed forward model with one task head I need you to: 1. Tune the hyperparameters of these models with a Bayesian approach 2. Demonstrate the performance of this model without PCA (currently PCA is used) through confusion matrix, training times, training validation loss and also AUC ROC for the classifiers 3. Use a feature reduction model that preserves feature importance unlike PCA and assess its performance 4. Compare its performance against classical ML approaches including: extra tree, KNN, random forest, SGD classifier and SVM, generating the same output metrics of performance
You need to build a multi fidelity surrogate model using Bayesian optimization. Low fidelity model will be built by using Xfoil High fidelity model will be built using starCcm+
Develop toulmin argumentation algorithm by bayesian
modified toulmin argumentation algorithm by Bayesian
Problem Solving, route finding, Informed search and Heuristics, uniformed search, Adversarial Search, Fuzzy logic, Dynamic Bayesian Networks & Markov chains,
Jupyter notebook bayesian modelling as discussed in file
Need a tutor who can guide on Bayesian statistics with examples using python
Please read the description below carefully before bidding. Modelling sensitivity to viral infections using a Bayesian Network
topics include: Probabilities Bayesian Decision Theory Maximum Likelihood Estimation (MLE) and Maximum a Posteriori Estimation (MAP) Derivation Integration Bayesian parametric Estimation Expectation maximization and mixture desnsity estimation Supervised Learning non-supervised learning Neural Networks Stochastic Methods Bayesian Belief networks non-parametric methods
topics include: Probabilities Bayesian Decision Theory Maximum Likelihood Estimation (MLE) and Maximum a Posteriori Estimation (MAP) Derivation Integration Bayesian parametric Estimation Expectation maximization and mixture density estimation Supervised Learning non-supervised learning Neural Networks Stochastic Methods Bayesian Belief networks non-parametric methods
The project is just about writing a simple Python code about an experiment using Bayesian statistics
The project consists of 3 assignments. The assignments are mostly PYTHON NOTEBOOKS except the first part of assignment 1 () which is about calculations of variables and functions. The tasks and the questions are well explained in the assignments themselves. Assignment 2 (assignm...questions are well explained in the assignments themselves. Assignment 2 () and assignment 3 () will need to import data sets (.txt files) that I will include together with the questions. Data sets for assignment 2: , and Data set for assignment 3: Assignment 2 is mainly about Machine Learning Assignment 3 is about Bayesian Statistics. Very important: The assignments must completed before December 10th. Thank you!
Hello I need a help with bayesian network. I have provided you a pdf file of theories. can you implement a theory into a programming application? programming language needs to be JAVA or Python. let me know. you can choose any topic inside this pdf file to do the programming. program does not need to be that much complicated
Hello I need a help with bayesian network. I will be providing you a pdf file of theories. can you implement a theory into a programming application? programming language needs to be JAVA or Python. let me know. you can choose any topic inside this pdf file to do the programming.
Hello I need a help with bayesian network. I will provide you a pdf file of theories. can you implement a theory into a programming application? programming language needs to be JAVA or Python. let me know. you can choose any topic inside this pdf file to do the programming.
looking for someone who is good in Python, mathematics, statistics, integration, differentiation, algrebra Concepts include: Probabilities Bayesian Decision Theory Maximum Likelihood Estimation (MLE) and Maximum a Posteriori Estimation (MAP) Derivation Integration Bayesian parametric Estimation Expectation maximization and mixture desnsity estimation Supervised Learning non-supervised learning Neural Networks Stochastic Methods Bayesian Belief networks non-parametric methods clustering
1. Objectives The purpose of this theme is to represent and solve problems with events probabilistic using Bayesian networks. The aspects pursued in this topic are: ■ understanding issues involving uncertainty in natural language ■ determining the variables and the connections between them ■ Correct representation of causal relationships between variables using a Bayesian network ■ the correct transposition of the probabilities associated with the variables in the Bayes network ■ calculation of probabilities based on the representation using the Bayes network ■ deploy a Bayes network using a dedicated application 2. Proposed problems The theme starts from 3 problems given in natural language. Every problem contains certain probabilistic events and relationships between event...
Want to know the most commonly used algorithms which can be applied to any data issue? Here's the list.
Open Source tools are an excellent choice for getting started with Machine learning. This article covers some of the top ML frameworks and tools.