Pattern Classification

Course Code: ECEM-208

Course Instructor: Dr. Shahid Mehraj Shah

Syllabus:

• Revision of Probability Theory: Dirichlet Distribution, Gaussian Distribution, Exponential Family
• Linear Regression: ML and Least Squaring, Bias-Variance Decomposition
• Logistic Regression: Two class/Multi-class cases, Probabilistic Generative Models, Probabilistic Discriminative Models, Bayesian Logistic Regression
• Neural Networks: Feed-forward Network, Error Back-propagation, Regularization, Bayesian Neural Network, Deep Learning
• Kernel Methods: Radial Basis Function Network, Gaussian Process, Support Vector Machine (SVM)
• Graphical Model: Bayesian Networks, Markov Random Field,
• Mixture Model: K-Means Clustering, EM Algorithm

Books:

• Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer Publishers (Click Here to Download the Book)

References:

• Richard O. Duda, Peter E. Hart, David G. Stork, "Pattern Classification", Wiley and Sons, Click here to download

Lecture Schedule

1. Mathematical Notation. Link to PDF.
2. Probability Theory-I. Link to PDF part-I, Link to PDF Part-II