machine learning theoretical

$30-250 USD

Imefungwa

Imechapishwa

about 3 years ago

$30-250 USD

Kulipwa wakati wa kufikishwa

topic/materical included are: From over-fitting to apparently complex methods which can work well, such as VC dimension and shattering sets. PAC bounds. Loss functions. Risk (in the learning theoretic sense) and posterior expected risk. Generalisation error. Supervised, unsupervised and semi-supervised learning. The use of distinct training, test and validation sets, particularly in the context of prediction problems. The Bootstrap revisited. Bags of Little Bootstraps. Bootstrap aggregation. Boosting. Big Data and Big Model – issues and (partial) solutions: The “curse of dimensionality”. Multiple testing; voodoo correlations, false-discovery rate and family-wise error rate. Corrections: Bonferroni, Benjamini-Hochberg. Sparsity and Regularisation. Variable selection; regression. Spike and slab priors. VC dimension Inequalities (hoeffding,Chernoff bound), concentration inequalities. Ridge Regression. The Lasso. The Dantzig Selector. Concentration of measure and related inferential issues. Metropolis hastings, gibbs sampling MCMC in high dimensions – preconditioned Crank Nicholson; MALA, HMC. Preconditioning. Rates of convergence.

Machine Learning (ML)

Statistics

Data Science

Kitambulisho cha mradi: 29855665

Kuhusu mradi

7 mapendekezo

Mradi wa mbali

Inatumika 3 yrs ago

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7 wafanyakazi huru wana zabuni kwa wastani $164 USD kwa kazi hii

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