Probability (self-paced)

This course, perfect for researchers and practitioners seeking a deep understanding of machine learning, covers the fundamentals, including regression and classification, through real-life dataset analysis.


Dr. Waleed A. Yousef
Adjunct Professor,
University of Victoria, Canada.


Rice, J.A., “Mathematical statistics and data analysis”. 3rd ed.


  • Single Variable Calculus

Videos & Materials

  • 26 lectures (24 hours)
  • Time-stamped videos
  • Lecture notes
  • Homeworks
  • TA

  • Discussion groups & TA support
  • TA-human private chat mode
  • No TA-GPT (under construction)
  • Certificate of Knowledge

    Awarded after passing a brief sample exam, which you may attempt multiple times.

    What will you learn?

    This standard course in probability theory is designed for students in applied sciences. It covers the fundamental principles of probability theory and emphasizes the importance of both mathematical rigor and intuition. To achieve this objective, the course includes comprehensive coverage of proofs, intuitive explanations of mathematical concepts, and numerous examples showcasing real-life applications and real datasets.

    By the end of the course, students will gain a solid understanding of basic probability concepts, providing them with comfort and proficiency when encountering such concepts in their future studies in computer science.

    The course will extensively cover the first six chapters of the book, including topics such as counting, the law of total probability, Bayes' rule, random variables, discrete and continuous random variables, moments, multivariate distributions, joint density functions, marginal distributions, covariance, functions of random variables, transformations, and the Central Limit Theorem.

    Course Content