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).

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.

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.

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.