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Machine Learning for Signal Processing
Introduction: Representing text, Sounds and Images text, speech, image, and video. Signal processing for feature extraction: for Text (BoW), Speech (LPC, Mel-frequency Capstral coefficients, STFT and Wavelet features), Images (HoG, BoVW, FV), Videos (BoVW).
Advanced Sensors And Interface Electronics
Introduction and Background of state-of-art sensing and measurement techniques. Contactless potentiometer (resistance-capacitance scheme) – Methodology,Interface Circuits, Overview of Flight Instrumentation. Analog Electronic Blocks, CMRR Analysis (Non-ideal opamps) of an Instrumentation Amplifier, Linearization circuits for single-element wheatstone bridges (application to strain gauge), Direct Digital Converter for Strain gauges, Signal conditioning for Remote-connected sensor elements.
Deep Learning for Computational Data Science
Description: Deep learning methods are now prevalent in the area of machine learning, and are now used invariably in many research areas. In recent years it received significant media attention as well. The influx of research articles in this area demonstrates that these methods are remarkably successful at a diverse range of tasks. Namely self driving cars, new kinds of video games, AI, Automation, object detection and recognition, surveillance tracking etc.
Pattern Recognition and Machine Learning
PR overview-Feature extraction-Statistical Pattern Recognition-Supervised Learning-Parametric methods-Non parametric methods; ML estimation-Bayes estimation-k NN approaches. Dimensionality reduction, data normalization. Regression, and time series analysis. Linear discriminat functions. Fishers linear discriminant, linear perceptron and Neural Networks. Kernel methods and Support vector machine. Unsupervised learning and clustering. K-means and hierarchical clustering. Ensemble/ Adaboost classifier, Soft computing paradigms for classification and clustering.
Operations Research
Introduction – linear programming – duality and sensitivity analysis – transportation and assignment problems – integer programming – network optimization models – dynamic programming – non-linear programming – unconstrained and constrained optimization – non-traditional optimization algorithms.