- Class Times:
- Tuesdays and Thursdays, 12:30–1:50pm
- First class:
- September 1, 2020
- Google Meet (see eClass)
- James Wright (firstname.lastname@example.org)
- James: Available after lectures, and by appointment.
- Ehsan: Wednesdays, 3–4pm in Google Meet (see eClass)
- Liam: Fridays, 11am–12pm in Google Meet (see eClass)
The field of machine learning involves the development of statistical algorithms that can learn from data, and make predictions on data. These algorithms and concepts are used in a range of computing disciplines, including artificial intelligence, robotics, computer vision, natural language processing, data mining, information retrieval, bioinformatics, etc. This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including how should one think about data; how can data be summarized; how models can be estimated from data; what sound estimation principles look like; how generalization is achieved; and how to evaluate the performance of learned models.
Remote delivery considerations
- Synchronous lectures
- Lectures will be delivered synchronously via Google meet (see eClass for the link)
- Lectures will be recorded, to enable asynchronous access by students with connectivity issues.
- Lecture recordings will be made available on eClass until the end of the term.
- Students may turn off their camera, and are requested to turn off their audio upon joining. Students are encouraged to ask questions during lecture, either out loud or via text chat if that is more comfortable.
- Online assignments and exams
- Both assignments and exams will be submitted electronically using eClass
- An online proctoring service will not be used, but
- We will check answers for similarity, and
- Students may be called upon to explain their answers verbally in an online meeting to the instructor and/or TAs. Students who cannot explain how they arrived at their solutions may not receive credit.
Assignments are distributed on eClass. They are to be handed in electronically via eClass by 11:59pm Mountain time on the due date. Late assignments will have 20% deducted for each day that the assignment is late, up to a maximum of three days late.
Submitting the work of another person as your own constitutes plagiarism. Academic honesty is taken very seriously: Suspected instances of ALL forms of cheating are referred to the Faculty of Science.
The rules for this course allow consultation collaboration. The specific rules for assignments (not exams) are:
- You may discuss assignments and solutions with your classmates.
- Limit discussion to an informal verbal level. DO NOT exchange written text or source code: you can discuss assignments, but don’t look at each other’s answers or give step-by-step instructions.
- You must list all of the other students that you discussed an assignment with.
- The written part of the assignments (including source code) must be completed individually.
Readings will be from the companion textbook, available as a PDF:
- Martha White, Basics of Machine Learning
Optional references and recommended readings:
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Machine Learning
- David Barber, Bayesian Reasoning and Machine Learning.
- C.M. Bishop, Pattern Recognition and Machine Learning
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning
- Quiz: 5%
- Midterm: 20%
- Final exam: 35%
- Assignments (3): 30%
- Thought Questions: 10%
Marks will be converted to letter grades based on a curve. There are no set grade boundaries.