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Training in Machine Learning


Syllabus for prescribed Training

Chapter 1: Introduction       (12 hrs)

  • Programing basics in python
  • Matpoltlib
  • Numpy
  • Scipy

Chapter 2: Mathematics for ML     (9 hrs)

  • Interpolation
  • Linear and Logistics Regression
  • Spectral Analysis
  • Probability
  • Random Variables
  • Approximation and  Basis Functions
  • Correlation
  • Convolution
  • Covariance
  • Multivariate Error Analysis and Distance Estimation

Chapter 3: Data Acquisition Technique     (6 hrs)

  • Sensing and Signal Conditioning
  • Acquisition of Logic Variables
  • IOT and Mobile data acquisition

Chapter 4: Data Preparation           (6 hrs)

  • Temporal and Spatial Filtering
  • Noise Reduction
  • Baseline Extraction
  • Interference and Artefact Detection and Suppression
  • Multivariate Normalization
  • Textual Variable stemming and stop word elimination

Chapter 5: Feature Extraction (6 hrs)       

  • Principal Component Analysis
  • Independent Component Analysis
  • Non-Linear dimensionality reduction (edge, corner, blob, direction, motion detection)

Chapter 6: Training and Classification      (6 hrs)

  • Linear and Logistic regression
  • Gradient Descent
  • clustering algorithms
  • Classification algorithms
  • ANN based systems
  • Classifier Evaluation (accuracy, precession and recall, f1 score and ROC)
  • Algorithm Selection

Practical assignments (15 hours)