这是一个加拿大的Python机器学习作业代写
Objectives:
1. [10 marks] The basic goal of the mini-project is for the student to gain first-hand experience in
formulating a task as a machine learning problem and have a rigorous practice of applying machine
learning algorithms.
2. [5 marks] The second goal (optional to undergrads) is to accomplish a non-trivial machine learning
project, such as replicating a recent top-tier machine learning publication (published at ICML, NeurIPS,
ICLR, etc.), proposing new models, and empirically analyzing machine learning models in a significant
way. Replicating a paper published at an unknown or non-machine learning venue may not constitute a
non-trivial project. The 5 marks count as bonus for undergrads but are included within 100 total marks
for grads.
Note that only one project is expected. A non-trivial project must also satisfy the basic requirements.
Team work
Collaboration of the course project is possible only if
1) all team members have already had first-hand experience,
2) they intend to do a non-trivial project, and
3) the team must have no more than three members.
Online lab sessions are a good opportunity for students to know each other and form collaboration
teams. The team has to apply in NOI before Jan 18 (extended to Jan 19). The application may be
declined if any of the team members does not have adequate machine learning background.
If teamwork is approved, the team members (name, ID, and email) and individual contributions must be stated
clearly in all submissions. All team members must upload the submissions to their own eClass assignments.
Basic Requirements [10 marks]:
● Formulating a task into a machine learning problem. The student CANNOT re-use any task in
coding assignments (namely, house price and MNIST datasets) as the course project.
● Implementing a training-validation-test infrastructure, with a systematic way of hyperparameter
tuning. The meaning of “training,” “validation,” “test,” and “hyperparameter” will be clear very
soon.
● Comparing at least three machine learning algorithms. In addition, include a trivial baseline (if
possible). For example, a majority guess for k-category classification yields 1/k accuracy. The
machine learning algorithms must be reasonable for solving the task, and differ in some way
(e.g., having different hyperparameters do not count as different machine learning algorithms).
General machine learning packages may be used for the course project. However, the student cannot use the
codebase specific to the task at hand and run a few scripts like “sh run.sh”.
Requirements for a non-trivial project [5 marks]:
A non-trivial project could be either replicating a recent machine learning paper that involves some
sophistication, proposing new models, or conducting empirically analyzing machine learning models in a
significant way.
Typically, a non-trivial project involves a significant amount of literature reading, programming and
conducting experiments. A student would not expect any bonus mark by trying some CNN/RNN models, or
applying existing code base to a new task in a straightforward way. If a student seeks non-triviality marks by
replicating a recent paper, the student should assume the code base of that paper does not exist.
If a student has doubt how non-trivial the project is, the student may ask how much mathematical and
algorithmic formulation there is.