UNIT 1:
Basics of pattern recognition
Design principles of pattern recognition system
Design principles of pattern recognition system
Pattern recognition approaches.
UNIT 2:
Bayesian Decision Theory Continuous Features
Discriminant functions for the normal density Maximum likelihood estimation.
The normal density- Discriminant functions
discriminant functions, and decision surfaces
Minimum error rate- classification- classifiers
UNIT 3:
Density Estimation - Parzen Windows
K-Nearest Neighbor Estimation - Nearest Neighbor Rule
Fuzzy clustering. NonMetric Methods
Introduction-Decision Trees- CART
Other Tree Methods Recognition with Strings
UNIT 4:
Anomaly detection– data driven methods
feature engineering – detection with data and algorithms
challenges using ML- response and mitigation
Malware Analysis: defining
feature generation – classification
UNIT 5:
Facing the Cybercrime Problem Head-on
Emerging Cybercrime Techniques
Understanding the People on the Scene
The Computer Investigation Process
Acquiring Data, Duplicating Data, and Recovering Deleted Files
Understanding Network Intrusions and Attacks.
