Overview

Lectures:

Tue & Thu 12:30pm - 1:50pm (CCIS 1-440 & Virtual)

Tutorial (Optional):

Thu 4:00pm - 5:00pm (CCIS 1-160 & Virtual)

Instructor:

Vlad Tkachuk (email: vtkachuk@ualberta.ca)

Office Hours:

Thu 2:15pm - 3:30pm (CSC 2-15)

TA Email:

cmput267@ualberta.ca

eClass:

Link

Piazza:

Link

Recordings:

Link

Course Notes:

Link

Syllabus:

Link

Please refer to the syllabus for the official course policies.

TAs and Office Hours

There will be TA office hours both in-person and virtual. You can sign up for 10-minute slots, using this Sheet.

Name	Day and Time	Location
Bahar Boroomand Ghahnavieh	Monday 9:00am - 10:00am	Virtual
Abdelrahman Elaraby	Monday 12:00pm - 1:00pm	CAB 313
Mehrshad Tavana	Monday 1:00pm - 2:00pm	CAB 313
Alireza Masoumian	Tuesday 9:00am - 10:00am	CAB 313
Aidan Bush	Tuesday 3:00pm - 4:00pm	Virtual
Guoqing Luo	Wednesday 9:00am - 10:00am	Virtual
Thang Duc Chu	Wednesday 2:00pm - 3:00pm	Virtual
Jai Riley	Wednesday 3:00pm - 4:00pm	Virtual
Vlad Tkachuk (Instructor)	Thursday 2:15pm - 3:30pm	CSC 2-15
Alex Ayoub	Friday 9:00am - 10:00am	CSC 2-18
Rohini Das	Friday 1:00 - 2:00pm	Virtual
Kushagra Chandak	Friday 4:00pm - 5:00pm	Virtual

Course Description

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.

Schedule

See the Schedule tab to see the topics covered and when graded work deadlines are.

Grading

Assessment	Weight	Date
Assignments (8, top 7 counted):	30% (4.29% each)	See Schedule tab
Midterm exam 1:	20%	Oct 8, 2024 in class (12:30pm - 1:45pm in CCIS 1-440)
Midterm exam 2:	20%	Nov 19, 2024 in class (12:30pm - 1:45pm in CCIS 1-440)
Final exam	30%	Dec 18, 2024 (3 hours, starts at 8:30am), date and time are tentative

At least 3 of the assignments will be coding assignments. We will be using Python in Google Colab. Resources to help you get started with Google Colab will be shared later in the course.

To do the assignments you will need: An internet connection, and a modern web browser (Chrome, Firefox, or Safari recommended).

Asking Questions and Getting Help

Students are encouraged to utilize Large Language Models (LLMs) like ChatGPT, Gemini, or Claude as a primary resource for course-related questions. This approach serves two purposes: familiarizing students with increasingly important AI tools and providing quick, comprehensive answers, including visual aids when necessary. However, it is crucial to remember that LLM outputs should not be blindly trusted; students must verify information if unsure of its accuracy.

This term we will be using Piazza for class discussion (please use your real name when signing up). The system is highly catered to getting you help fast and efficiently from classmates, TAs, and the instructor. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. You can also post your questions anonymously. You may see a prompt about contributing, but you are NOT REQUIRED to pay anything to participate.

Some questions need to be asked privately, either because they are personal or because they might reveal too much of an answer. For these questions email the TAs using the email address cmput267@ualberta.ca. We ask that you only use this email when communicating with the TAs; do not contact TAs via their personal email.

If the question is about course organization or issues, like missing exams or personal issues, then you should directly email the instructor (Vlad Tkachuk, email: vtkachuk@ualberta.ca).