**2018-2019 IEEE Machine Learning Projects**

**Call:9591912372**

Email: [email protected]

Email: [email protected]

1.Dynamical Component Analysis (DYCA): Dimensionality Reduction for High-Dimensional Deterministic Time-Series

2.Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD)

3.Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization

4.Evaluation of Loss Functions for Estimation of Latent Vectors from GAN

5.Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging

6.Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

7.Graph-Regularized Fast Low-Rank Matrix Approximation Using The NystrÖM Method for Clustering

8.K-SVD with a Real ℓ0Optimization: Application to Image Denoising

9.Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks

10.Speech Emotion Recognition Using Cyclostationary Spectral Analysis

**2018-2019 IEEE Projects for CSE Machine Learning**

1.Dynamical Component Analysis (DYCA): Dimensionality Reduction for High-Dimensional Deterministic Time-Series 2.Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD)

3.Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization

4.Evaluation of Loss Functions for Estimation of Latent Vectors from GAN

5.Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging

**2018-2019 Machine Learning Projects for Mobile Applications**

6.Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

7.Graph-Regularized Fast Low-Rank Matrix Approximation Using The NystrÖM Method for Clustering

8.K-SVD with a Real ℓ0Optimization: Application to Image Denoising

9.Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks

10.Speech Emotion Recognition Using Cyclostationary Spectral Analysis

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Machine learning projects for final year students