Supervised learning: classification

• 114ML Predicting whether US resident has health insurance or not based on survey of American households

Spec: Benchmark multiple classification models to recognize multidimensional data.

Language: Python (Keras-Tensorflow, Scikit-learn), Matlab (Deep Learning & Neural Networks Toolbox, Machine Learning Toolbox).

• 118 Text classification using bag of words and SVM

Spec: Classify text using word frequency counts as predictors.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox, Text Analytics Toolbox).

• 122 Sentiment detection using pretrained word embedding and SVM

Spec: Classify text by mapping words into numerical vectors.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox, Text Analytics Toolbox).

• 71 Recognizing neurological disease on MRI image using TL with feature extraction from AlexNet

Spec: TL (transfer learning) from a network pretrained on images of one set of classes to a network which is supposed to recognize images of another set. The TL is done by feature extraction based on integration of neural network (CNN) with machine learning (SVM).

Language: Python (Keras-Tensorflow), Python (Scikit-learn), Matlab (Deep Learning & Neural Networks Toolbox).

• 03 Extracting classification features from fetal heart rate waveform and classifying fetal clinical condition

Spec: Integrate signal processing and machine learning.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox, Signal Processing Toolbox).

• 46 Deer detection from car using feature extraction from pretrained CNN

Spec: TL (transfer learning) from a network pretrained on images of one set of classes to a network which is supposed to recognize images of another set. The TL is done by feature extraction based on integration of neural network (CNN) with machine learning (SVM).

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Computer Vision Toolbox, Machine Learning Toolbox).

• 47 Bicycle detection from car using bag of visual words & SVM

Spec: Integrate computer vision and machine learning.

Language: C++ (OpenCV), Matlab (Computer Vision Toolbox, Machine Learning Toolbox).

• 57 Birds detection from airplane using feature extraction from pretrained CNN

Spec: TL (transfer learning) from a network pretrained on images of one set of classes to a network which is supposed to recognize images of another set. The TL is done by feature extraction based on integration of neural network (CNN) with machine learning (SVM).

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Computer Vision Toolbox, Machine Learning Toolbox).

• 58 Birds detection from airplane using bag of visual words & SVM

Spec: Integrate computer vision and machine learning.

Language: C++ (OpenCV), Matlab (Computer Vision Toolbox, Machine Learning Toolbox).

• 50 Recognizing text on speed limit road sign using image retrieval

Spec: Investigate image retrieval (based on bag of visual words & SVM) for text recognition.

Language: C++ (OpenCV), Matlab (Computer Vision Toolbox).

Supervised learning: regression

• 02 Investigating Gaussian Process Regression model

Spec: Investigate Gaussian Process Regression model to predict continuous values.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox).

Unsupervised learning: cluster analysis

• 91 Image segmentation using super-pixels

Spec: Integrate image processing (texture analysis) and machine learning (k-means).

Language: C++ (OpenCV), Matlab (Image Processing Toolbox).

• 01 Clustering vital sign features using GMM (Gaussian mixture models)

Spec: Break data into multivariate normal density clusters.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox).

• 44 Detecting necrotic muscle fiber on biopsy image using k-means

Spec: Integrate image processing and machine learning.

Language: C++ (OpenCV), Matlab (Machine Learning Toolbox, Image Processing Toolbox).

Unsupervised learning: PCA (principal component analysis)

• 04 Reducing dimensionality of vital sign features using PCA

Spec: Reduce number of continuous predictors by their decorrelation.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox).

Unsupervised learning: NCA (neighborhood component analysis)

• 05 Selecting influential features for fingerprint identification using NCA

Spec: Reduce number of categorical predictors by their ranking.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox).

Unsupervised learning: RICA (reconstruction independent component analysis), SF (sparse filtering)

• 87 Recognizing hand-written digits preprocessed by RICA & SF

Spec: Investigate RICA & SF for image classification.

Language: Matlab (Machine Learning Toolbox).

• 06 Adaptive noise cancellation of fetal heart signal using RICA

Spec: Investigate RICA for adaptive noise cancellation.

Language: Matlab (Machine Learning Toolbox).

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