CMPUT 466/566 (Fall 2024) Syllabus
Machine Learning Essentials
Course Format: The course is offered in person only. When the IT infrastructure allows, the lectures will be broadcasted and recorded. However, the instructor cannot guarantee that IT will work well, in which case pre-recorded videos will be released as a replacement. Exams will be based on in-person lectures.
Lecture time and classroom:
T, Th 12:30PM - 1:50PM, Sep 3 - Dec 9
● No course activities during the reading week
Online lecture hall: https://meet.google.com/vqo-vkxv-osy
Dial-in: (US) +1 337-573-0059 PIN: 584 572 100#
Lab session (in-person only): Monday 5–7:50PM
● 5-6PM: TA’s office hours (CCIS L1-140)
● 6-7:50PM: Unattended. TAs will open appointment slots for QA.
Instructor/TA office hours:
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With whom
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Email
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Open door
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By appointment
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Lili Mou (instructor)
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lmou
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Thursday, 3-4PM
ATH4-08
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as appropriate
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Zijun Wu
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zijun4
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Monday, 5-6PM CCIS1-160
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Tue (2-2:30 PM)
In-person: CSC3-26 Online: meeting link
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Nicolas Rebstock
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nrebstoc
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Thu (3-3:30 PM)
In-person: CSC3-26 Online: meeting link
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Haruto Tanaka
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haruto
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Thu (10-10:30 AM) Online: meeting link
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Yu Wang
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yu35
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Mon (9-9:30 AM)
Online: meeting link
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Tian Tian
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ttian
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Tue (2:50-3:20 PM)
Online: meeting link
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● Office hours start from the second week. No office hours on statutory holidays and during the reading week.
● If a student wishes to make an appointment with a TA (10min each slot), they will send an email to the TA before the date.
● Office hour schedule may be changed depending on the need.
Students are encouraged to reach out to the instructor if TAs’ answer is not satisfactory.
Notes:
The instructor and TAs will not answer assignment-related questions before the solution is released.
COURSE CONTENT
Course Description:
Machine learning teaches a machine to learn from previous experience and makes a
prediction for (possibly new) data. This course covers standard materials of a “ Machine Learning” course, such as linear regression, linear classification, as well as non-linear models. In the process, we will have a systematic discussion on issues such as training criteria, inference criteria, bias-variance tradeoff, etc. The goal of the course is to build a solid foundation of machine learning; so there would be intensive math derivations in lectures, assignments, and exams.
Course Prerequisites:
Please fulfill the departmental requirements.
The department asks instructors normally not to waive prerequisites.
Course Objectives and Expected Learning Outcomes:
By the end of this course, the student will understand the foundations of machine learning and gain experience in machine learning applications.
Official textbook: Bishop, Pattern Recognition and Machine Learning.
The instructor will provide lecture notes, which may also suffice. If not, please use the above textbook. [survey on textbooks]
References: link
Tentative topic list:
Linear regression
● Mean squared error (as heuristics)
● Closed-form. solution
● Gradient descent
● Maximum likelihood estimation
● Maximum a posteriori training
● Bias-variance tradeoff
● Train-validation-test framework
Linear classification
● Discriminative model: Logistic regression
● Multi-class softmax
● Maximum a posteriori inference
● Generative model: Naïve Bayes
● Discriminant model: Linear SVM (bonus lecture)
Nonlinear models
● Neural networks
● Kernels methods: Non-linear SVMs (bonus lecture)