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Data Mining and Machine Learning Types of Multi-Layer Perceptron Peter Jančovič Slide 1 Data Mining and Machine Learning Feed-forward Neural Networks Multi-Layer Perceptron – Feed-Forward Neural Network Input Layer (Input Units) Artificial neuron Hidden Layers (Hidden Units) Slide 2 Output Layer (Output Units) Data Mining and Machine Learning What can you do with a (D)NN? […]

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Data Mining and Machine Learning Clustering I Peter Jančovič Slide 1 Data Mining and Machine Learning Data Mining  Objective of Data Mining is to find structure and patterns in large, abstract data sets – Is the data homogeneous or does it consist of several separately identifiable subsets? – Are there patterns in the data?

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

程序代写 CS代考 / , ,

Data Mining and Machine Learning Topic Analysis Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Statistical modelling of topics  Identifying topics in a document – Latent Dirichlet Allocation (LDA)  Topic Spotting – Salience and Usefulness – Example: The AT&T “How May I Help You?” system Slide 2 Data Mining and

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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 HMM Training Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Reminder: Maximum Likelihood (ML) parameter estimation – ML for Gaussian PDFs and for GMMs  ML for HMMs – Viterbi-style training – Baum-Welch algorithm Slide 2 Data Mining and Machine Learning Fitting a Gaussian PDF to Data

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Data Mining and Machine Learning Speech Recognition using HTK Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives  Building an ASR system using Hidden Markov Model Toolkit (HTK) – Feature Representation – Training – Recognition (Testing)  Introduction to Perl Slide 2 Data Mining and Machine Learning ASR system using HTK  Hidden

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