**2019 Deep Learning Projects**

## **2018 Machine Learning Projects for Final Year**

In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.

**IEEE Projects on Machine Learning**Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

## **2018-2019 Machine Learning Projects for Mtech**

In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to

**IEEE Projects on Machine Learning**find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

### **2018-2019 Machine Learning Projects for Engineering Students**

In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.

**IEEE Projects on Machine Learning**Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

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Email: projectsatbangalore@gmail.com

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

Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data",

The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning,and focuses on exploratory data analysis through unsupervised learning.In its application across business problems, machine learning is also referred to as predictive analytics.

Email: projectsatbangalore@gmail.com

## 2018-2019 IEEE Projects on Deep Learning

**2018-2019 IEEE Machine Learning Projects**

__Mtech Projects on Machine Learning__Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems.

…__IEEE Projects on Machine Learning__Kernel support vector machines (SVMs) deliver state-of-the-art results in many real-world nonlinear classification problems, but the computational cost can be quite demanding in order to maintain a large number of support vectors. Linear SVM, on the other hand, is highly scalable to large data but only suited for linearly separable problems. In this paper, we propose a novel approach called low-rank linearized SVM to scale up kernel SVM on limited resources. Our approach transforms a nonlinear SVM to a linear one via an approximate empirical kernel map computed from efficient kernel low-rank decompositions. We theoretically analyze the gap between the solutions of the approximate and optimal rank.

…__Machine Learning Projects in Bangalore__The abundance of image-level labels and the lack of large scale detailed annotations (e.g. bounding boxes, segmentation masks) promotes the development of weakly supervised learning (WSL) models. In this work, we propose a novel framework for WSL of deep convolutional neural networks dedicated to learn localized features from global image-level annotations. The core of the approach is a new latent structured output model equipped with a pooling function which explicitly models negative evidence, e.g. a cow detector should strongly penalize the prediction of the bedroom class. We show that our model can be trained end-to-end for different visual recognition tasks: multi-class and multi-label classification, and also structured average precision (AP) ranking. Extensive experiments highlight the relevance of the proposed method: our model outperforms state-of-the art results on six datasets.

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**2018-2019 Machine Learning Projects in Bangalore****IEEE Projects on Machine Learning**

Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", **IEEE Projects on Machine Learning**in order to make predictions or decisions without being explicitly programmed to perform the task.**2018-2019 Machine Learning Projects in Bangalore**

The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning,and focuses on exploratory data analysis through unsupervised learning.In its application across business problems, machine learning is also referred to as predictive analytics.

**IEEE Projects on Machine Learning**Different from these approaches on fusion of stereo and activedepth sensors, our method focuses on generating high-resolutiondepth video with one color video (not stereo) and its corresponding low-resolution depth video. For that, we proposea weighted mode filtering (WMF) based on a joint histogram.The weight based on similarity measure between reference and neighboring pixels is used to construct the histogram, and a final solution is then determined by seeking a global mode on the histogram**
convolution filter based live Traffic sign Detection
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## 2018-2019 IEEE projects on machine learning

**Deep Learning Projects with Matlab/TensorFlow pdf**Deep Learning Projects Using Keras