data mining

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程序代写 CS代考 / , ,

Data Mining and Machine Learning Decision trees Peter Jančovič Slide 1 Data Mining and Machine Learning Slide 2 – Types of question – Automatic construction of DTs from data – Example from Speech Recognition: Phone Decision Trees Data Mining and Machine Learning Outline of lecture  Introduction to Decision Trees (DTs) – A third approach […]

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程序代写代做代考 data mining algorithm GMM Data Mining and Machine Learning

程序代写 CS代考 / , ,

Data Mining and Machine Learning K-Means Clustering Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  To explain the need for K-means clustering  To understand the K-means clustering algorithm  To understand the relationships between: – Clustering and statistical modelling using GMMs – K-means clustering and E-M estimation for GMMs Slide 2

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程序代写代做代考 data mining Data Mining and Machine Learning

程序代写 CS代考 /

Data Mining and Machine Learning Introduction to Artificial Neural Networks Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Introduce Artificial Neural Networks (ANNs)  Feed-forward ANNs – Multi-Layer Perceptrons (MLPs)  Basic MLP calculations  Geometric interpretation of MLPs Slide 2 Data Mining and Machine Learning Artificial Neural Networks  (Artificial) Neural

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程序代写代做代考 data mining algorithm Data Mining and Machine Learning

程序代写 CS代考 / ,

Data Mining and Machine Learning Learning MLP Weights using Error Back-Propagation Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Outline of the MLP training – The error function – Optimisation by gradient decent  The Error Back-Propagation (EBP) – Calculating the derivatives – Bringing everything together – Summary of the EBP algorithm

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程序代写代做代考 data mining Hidden Markov Mode Bayesian algorithm Data Mining and Machine Learning

程序代写 CS代考 / , ,

Data Mining and Machine Learning HMM Adaptation Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  So far we talked about Maximum Likelihood training for HMMs (the E-M algorithm) – Viterbi-style training – Baum-Welch algorithm  In this session, we talk about HMM adaptation: – Maximum A-Posteriori (MAP) estimation – Maximum Likelihood Linear

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程序代写代做代考 data mining Data Mining and Machine Learning

程序代写 CS代考 /

Data Mining and Machine Learning Statistical Modelling (1) Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Review basic statistical modelling  Review the notions of probability distribution and probability density function (PDF)  Gaussian PDF  Multivariate Gaussian PDFs  Parameter estimation for Gaussian PDFs Slide 2 Data Mining and Machine Learning

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程序代写代做代考 data mining Data Mining and Machine Learning

程序代写 CS代考 /

Data Mining and Machine Learning Application of HMMs for ASR: Feature representation of speech Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Front-end analysis for ASR – feature representation of speech – To understand motivation and stages for ‘typical’ parameterisation of speech signals used for ASR – Mel Frequency Cepstral Coefficients (MFCCs)

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程序代写代做代考 data mining algorithm GMM Data Mining and Machine Learning

程序代写 CS代考 / , ,

Data Mining and Machine Learning Statistical Modelling (2) Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  In – – –  In – – part 1 of this topic we Reviewed univariate Gaussian PDF Introduced multivariate Gaussian PDF Introduced maximum likelihood (ML) estimation of Gaussian PDF parameters this part, we will Introduce

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程序代写代做代考 data mining data structure information retrieval Data Mining and Machine Learning

程序代写 CS代考 / , ,

Data Mining and Machine Learning Lecture 4 TF-IDF Similarity, the Index and an Example Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Review IDF, TF-IDF weighting and TF-IDF similarity  Practical considerations  The word-document index  Example calculation  Assessing the retrieval Slide 2 Data Mining and Machine Learning Summary of

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程序代写代做代考 database data mining deep learning information retrieval algorithm compiler Data Mining and Machine Learning

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Data Mining and Machine Learning Lecture 2 Statistical Analysis of Texts Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Understand different approaches to text-based IR – Rationalism vs Empiricism  “Bundles of words” approaches  Introduction to zipf.c  Statistical analysis of word occurrence in text  Zipf’s Law  Examples Slide

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