Machine Learning Assignment Help

Machine learning is the science of getting computers to act without being explicitly programmed. Machine Learning assignment help provide introduction to machine learning, datamining, and statistical pattern recognition. It mainly includes (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

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  • A Hierarchical Face Recognition Algorithm
  • A Reformulation of Support Vector Machines for General Confidence Functions
  • Accurate Probabilistic Error Bound for Eigenvalues of Kernel Matrix
  • Applications of Machine Learning
  • Automatic Choice of Control Measurements
  • Averaged Naive Bayes Trees: A New Extension of AODE
  • Basic Machine Learning Models
  • Bayesian modeling
  • Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes
  • Building Advanced Models with Graphical Models
  • Building Advanced Models with Kernel Methods
  • Collaborative Filtering
  • Community Detection on Weighted Networks: A Variational Bayesian Method
  • Conditional Density Estimation with Class Probability Estimators
  • Conditional Random Fields
  • Context-Aware Online Commercial Intention Detection
  • Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model
  • Coupled Metric Learning for Face Recognition with Degraded Images
  • Density Ratio Estimation: A New Versatile Tool for Machine Learning
  • Dimensionality reduction
  • Estimating Likelihoods for Topic Models
  • Factorization of distribution according to BN
  • Feature Selection via Maximizing Neighborhood Soft Margin
  • Hidden Markov Models: Tutorial
  • Improving Adaptive Bagging Methods for Evolving Data Streams
  • Kalman Filter, Hidden Markov Models, Conditional Random Fields
  • Kernel methods and random features
  • Kernel PCA, clustering, canonical correlation analysis
  • Kernels, kernel classifier and regression
  • Latent Dirichlet Allocation
  • Learning Algorithms for Domain Adaptation
  • Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
  • Learning theory
  • Linear Time Model Selection for Mixture of Heterogeneous Components
  • Local Markov Assumption encoded by BN
  • Machine Learning and Ecosystem Informatics
  • Maximum entropy
  • Max-margin Multiple-Instance Learning via Semidefinite Programming
  • Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
  • Multi task learning
  • On Compressibility and Acceleration of Orthogonal NMF for POMDP Compression
  • Parameter learning from partially observed data --- EM
  • Probabilistic Matrix Factorization using MCMC
  • Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification
  • Real world applications
  • Reinforcement learning
  • Representation of Bayesian Networks
  • Representation of undirected GM
  • Review of probability and conditional independence
  • Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
  • Semi supervised learning
  • Statistical learning algorithms
  • Tensor data analysis
  • Transfer Learning beyond Text Classification
  • Two sample test and measure of dependence beyond vector data
  • Unified view of directed and undirected GM

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