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)