Course Description: Pattern Recognition is a fundamental area of study within the field of artificial intelligence and machine learning, focusing on the automatic discovery and identification of regularities or patterns in data. This course provides an in-depth exploration of the principles, techniques, and applications of pattern recognition.

Course Objectives:

  • To introduce students to the basic concepts and terminology of pattern recognition.
  • To familiarize students with various pattern recognition techniques and algorithms.
  • To develop students' understanding of the theoretical foundations underlying pattern recognition.
  • To enable students to apply pattern recognition techniques to solve real-world problems across diverse domains.
  • To provide students with hands-on experience through practical assignments and projects.

Topics Covered:

  1. Polynomial Regression Analysis
  2. Logistic Regression (binary, categorical and multi-label classifications)
  3. Decision Tree
  4. Ensemble Learning Methods
  5. Support Vector Machines (SVM)
  6. Clustering Techniques (Kmeans, SOM)
  7. Multi-Layer Perceptron (MLP) - Backpropagation Algorithm 
  8. Radial Basis Function Network (RBFN)
  9. Recurrent Neural Network (LSTM, GRU, Elman Network, Bi-directional layers)
  10. Deep Convoutional Neural Network (DCNN)
  11. Transfer Learning