MECH-8290-43

Course Description 

Broad introduction to machine learning, datamining, and statistical pattern recognition. The intention of the course is to familiarize students with different topics in the field, explain concepts, and implement the learning in numerous case-studies. This course will try to cover (i) Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction)

Class and lab information 

• Lecture:  3 hours/week 

• Additional, approximate study hours: 5 hours/week, as student requires

• Estimated division of Learning hours

  • hands-on labs and activities: 10%

  • group work: N/A

  • lecture: 20%

  • individual work (including homework, project, and tutorials): 50%

  • class discussion (during lecture time): 10%

• Credit weight: 3.0

• Course format: face-to-face content summaries, examples, and questions with online and textbook-based readings, videos, tutorials, etc.

• Pre-requisites

  • You should understand basic probability and statistics, algebra, and calculus. 

  • For programming, you should have some background in programming, and it would be helpful if you know Matlab or Python. During the course Python is mostly used and some tutorials will be done in class. Your project code must be in Python.

Resources  

• Course Blackboard site: available via student Blackboard login

• Interactive in-class activities via tools discussed at the beginning of the course.

  • There should be no costs to students for using these interactive tools.

• Primary text

  • Bishop, C. M. (2013). Pattern recognition and machine learning. New York: Springer.

• Additional resources

  • Murphy, K. P. (2013). Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press.

  • Mello, R. F., & Ponti, M. A. (2018). Machine learning: A practical approach on the statistical learning theory. Cham, Switzerland: Springer.

• Web resources

  • Organizations: Leddy Library, Knovel

  • Beautiful Decision Trees in Python

  • The Python Gallery - collection of hundreds of charts made with Python

Course Schedule 

• The following course schedule is approximate. (L) for lecture and (T) for tutorial. The readings are from Bishop.