This course (EE-612) presents fundamental tools used in statistical pattern recognition ranging from the most basic to more advanced (e.g. Logistic Regression, Principal Component Analysis, Linear Discriminant Analysis, Multi-Layer Perceptrons, Gaussian Mixture Models, Hidden Markov Models and Super Vector Machines). This course can serve as a pre-requisite for more advanced course on Machine Learning.

Outcomes: this course provides in-depth understanding in Statistical Pattern Recognition as well as concrete tools to PhD students for their work. This course could serve as a pre-requisite for more advanced courses such as Machine Learning, Graphical Models, Statistical Sequence Processing and Computational perception using multimodal sensors.

Keywords: Pattern Recognition, Machine Learning, Linear models, PCA, LDA, MLP, SVM, GMM, HMM.


  • Lectures: 32 hours (8 lectures)
  • Labs: 24 hours (5 labs)
  • Grading: lab assignments (35%) and final project (65%)
  • Room: CO 121
  • Thursdays, from 9:15 to 13:00 with a break in the middle
  • See attached PDF for specific lecture information

Required prior knowledgeLinear algebra, Probabilities and Statistics, Signal Processing, Python Programming

Resources: In general the slides of a lecture are available after the lecture (later in the day), while the lab material is available before the lecture (early morning), through Moodle.