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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.
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.
Foundations of Biological Neural Networks and Artificial Neural Networks (Learning, Generalization, Memory, Abstraction, Applications), McCulloch-Pitts Model, Historical Developments.ANN Architectures, Learning Strategy (Supervised, Unsupervised, Reinforcement), Applications: Function Approximation, Prediction, Optimization. Associative Memories: Matrix memories, Bidirectional Associative Memory, Hopfield Neural Network.
Optimization: Need for unconstrained methods in solving constrained problems, Necessary conditions of unconstrained optimization, Structure methods, Quadratic models, Methods of line search, Steepest descent method, Quasi-Newton methods: DFP, BFGS, Conjugate-direction methods:, Methods for sums of squares and nonlinear equations ,Linear Programming: Simplex Methods, Duality ii LPP, Transportation problem, Nonlinear programming: Lagrange Multiplier, KKT conditions, Convex programming.
Meaning and importance of simulation and modelling, classification of models, Variables and problem formulation, performance measures, Data collection and analysis, SIMSCRIPT language concept: general syntax, Discrete event modelling, process and resources, timing and pending list, accumulate and tally, process instance and object oriented aspects, sets and data structures, Probability distribution, Random number and random variant generation. Input modelling and output analysis.
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