CMPUT 367 (Fall 2022)

Intermediate Machine Learning

Syllabus

Class Times:
Tuesdays and Thursdays, 15:30–16:50 (3:30 pm - 4:50 pm)
First class:
September 1, 2022
Location:
CAB 243
Instructor:
Martha White (whitem at ualberta.ca)
Lab:
No Lab.
eClass
https://eclass.srv.ualberta.ca/course/view.php?id=71307
Syllabus
A pdf including the same information as below.
Textbook
The Intermediate Machine Learning Notes.

TAs

(in alphabetical order)

Office hours:

Listed on eClass.

Course Objective

Machine Learning is all about analyzing high-dimensional data. The goal for this second course in machine learning is to expand on the foundations from the first course. We will revisit several of the concepts–including how models can be estimated from data; sound estimation principles; generalization; and evaluating models–but with the additional nuances from handling high-dimensional inputs. Topics include: optimization approaches (constrained optimization, hessians, matrix solutions), kernel machines, neural networks, dimensionality reduction, latent variables, feature selection, more advanced methods for assessing generalization (cross-validation, bootstrapping), introduction to non-iid data and missing data.

This course relies on the concepts in CMPUT 267 - Basics of Machine Learning.

Overview

Learning Outcomes

By the end of the course, you should understand…

By the end of the course, you will have improved your skills in…

Topics

Knowledge Prerequisites

This course follows CMPUT 267, and relies on the understanding of the basic concepts in ML taught in that course. We will review many of these concepts, but now in more advanced settings (e.g., maximum likelihood for mixture models). The course relies on more knowledge in calculus and linear algebra than was needed for CMPUT 267. The numerical methods course (CMPUT 340) is a complementary and useful course for CMPUT 367, and so is a recommended co-requisite. An excitement to understand the mathematics underlying machine learning is a must.

Pre-requisites

Office hours

There are no labs, but TAs will host office hours for question and answering.

Readings / Notes

You are expected to read the corresponding sections about a class’s topic from notes before class as each class will discuss each topic in more detail and address questions about the material.

All readings are from the (in progress) machine learning notes. These are designed to be short, so that you can read every chapter. I recommend avoiding printing these notes, since later parts of the notes are likely to be modified (even if only a little bit).

Grading

Marks will be converted to Letter Grades at the end of the course, based on relative performance. There are no set boundaries, because each year we modify exams and there is some variability in performance. Set boundaries would penalize students in a year where we inadvertently made a question too difficult. A good indicator for final performance is performance on the exams, which are a large percentage of the grade. If you fail both exams (less than 50% on both), then you will likely get an F in the course.

Late Policy

Any late work will not be accepted and will receive 0 marks.

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 https://www.deanofstudents.ualberta.ca/en/AcademicIntegrity.aspx.