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