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Basics of Machine Learning Course


For the current schedule and to see the current set of notes.

Syllabus for CMPUT 296

Update on Syllabus: The syllabus is largely unchanged. The only two important changes are that

  1. The weight for the final has been reduced to 30%, and weight increased to 7% for the quiz and 23% for the midterm.

  2. I am making a clear cut-off to get a pass (and so get Credit in the course), of 55%.

Note the final will take place at the same time as before, but will now be an online exam on eClass. You will be expected to connect with a TA to check your one-card and show that you have logged on. Details for the online exam will be given on eClass.

See here for the full syllabus as a pdf.

Time and Location

Tuesday and Thursday, 2:00 - 3:20 p.m.

CCIS 1 140


Martha White

Office: ATH 3-05




(in alphabetical order)

The TAs are fantastic, and knowledgeable in machine learning; you should definitely ask them questions if you are stuck or to further your knowledge. At the same time, please respect all TAs time. There is a large class, and you should restrict meetings with TAs to about 15 minutes at a time (no more than 30 minutes).

Office hours

Lab times and locations

Labs are not mandatory, and will basically be run as office hours with the TAs. Each week the TAs might present some background material, and any clarifications on material or assignments. The TAs will also supplement with office hours outside this time, if needed.

Course Objective

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.


Main notes will be provided in class.


Bonus percent of 1% can be obtained from in-class participation. Mainly, I will reward you for asking a question in class.

Marks will be converted to Letter Grades based on a curve. There are no set boundaries. A good indicator for final performance is performance on the exams, which are a large percentage of the grade. If you fail all three exams (less than 50% on all three), then you will likely get an F in the course.

Late Policy and Academic Honesty

All assignments and exams are individual, except when collaboration is explicitly allowed. All the sources used for problem solution must be acknowledged, e.g. web sites, books, research papers, personal communication with people, etc. Academic honesty is taken seriously; for detailed information see