Course Information

Administrativia

  • Instructor: Sebastien Roch

  • Term: Spring 2020

  • Time: TuTh 11:00AM - 12:15PM

  • Location: 2255 Engineering Hall

  • Prerequisites: One linear algebra course (MATH 320, 340, 341, 375 or COMP SCI/E C E/M E 532) and one probability course (MATH/STAT 309, 431, MATH 531, STAT 311 or E C E 331) and one proof-based course (MATH 322, 341, 375, 376, 421, 467, 521); or graduate/professional standing or member of Pre-Masters Mathematics (Visiting International) Program. (NOTE: Math 322 will be accepted for the third requirement, but an exemption from the instructor will be needed.)

  • Text: Lecture notes in the form of Jupyter notebooks will be posted below.

  • Grades: will be based on midterm exams, homework assignments and a final project.

  • Syllabus: will be posted on Canvas.

Textbooks

We will use the following textbooks available online (which will be complemented by lecture notes and notebooks above):

  • [Sol] Solomon, Numerical algorithms, CRC Press, 2015 (Chaps 4-7)

  • [Bis] Bishop, Pattern Recognition and Machine Learning, Springer, 2006 (Chaps 2, 8, 9, 13)

  • [Wri] Wright, Optimization Algorithms for Data Analysis, in: The Mathematics of Data, AMS, 2018 (Sections 2-4)