Dr. Jeff Fortuna

Classification, association, prediction and clustering of data through the use of machine learning algorithms – from introductory approaches to recent advances. Evaluation metrics. Meta learning. Deep Learning.

Statistical Data Analysis, Linear Regression using OLS, MLE and FLD, Linear Classification using the perceptron, neural networks and the SVM, Big Data Architectures.

3 unit(s) Neural network architectures and training, parallel implementations and implementations using accelerated hardware, convolutional neural networks, recurrent neural networks and deep learning. Three lectures, one term; completely virtual Prerequisite(s): Registration in Level IV Software Engineering Technology This course is graded on a pass/fail basis.

3 unit(s) Classification, association, prediction and clustering of data. Decision trees. Bayesian probability. Supervised and unsupervised learning. Three lectures, one lab; one term; completely virtual Prerequisite(s): SFWRTECH 4DA3 and registration in Software Engineering Technology This course is graded on a pass/fail basis.

3 unit(s) Linear approaches to regression and classification – selection and shrinkage methods, linear discriminant approaches, big data system implementations. Three lectures, one term; completely virtual Prerequisite(s): SFWRTECH 3ST3 or ENGTECH 3ST3 or 3ES3, SFWRTECH 4MA3, and registration in Software Engineering Technology, OR ENGTECH 2MA3 and 3ES3, and registration in Biotechnology, or MECHENG 4RO3 or IBEHS 4AO3 and registration in any Mechanical Engineering Program. This course is graded on a pass/fail basis.

3 unit(s) Models of parallel computation. Performance measurement. Shared memory and synchronization. Parallel data structures, searching and sorting. Scheduling and work distribution. Three lectures, one term; completely virtual Prerequisite(s): Registration in Level IV Software Engineering Technology This course is graded on a pass/fail basis.

3 unit(s) Project-based course covering computer programming. Pointers, structured data, object-oriented concepts, inheritance, polymorphism, abstract class and overloading functions. Two lectures, one lab (two hours); second term Prerequisite(s): ENGTECH 1CP3 and registration in Automation Engineering Technology or Automotive and Vehicle Engineering Technology