Course Information

Lectures
Academic integrity
Guidance on the use of Generative AI
Late work, Extensions, & Special Circumstances

Lectures

The goal of this course is to introduce students to basic algorithms for learning from example data, focusing on classification and clustering problems. This course is intended for MSc students. It is delivered using an Inverted Classrom method. It should NOT be taken with MLPR, which covers much of the same material, or if you have taken IAML in the past.

The syllabus for this course is defined by the lecture slides, the tutorial questions and the labs. Any items from any of those sessions may be examined on. The required understanding of a subject may go beyond what is simply presented in the slides: you should know how to apply it and the more detailed context. The course texts and the videos can help with that.

NOTE: This course is not taught in the traditional lecture style. The expectation is that you take more control of your education in this course. This means that you will have to watch the lecture videos in your own time. This material is assessable, except where noted. It is very important that you watch the videos for the class meeting topics at least a day before the class. Each class will consist of several parts: 1) discussing any questions about the videos that you either suggest in advance or raise in class on the day, 2) simple non-assessed exercises to explore the issues raised in the videos that you have just watched, and 3) examples going further into depth in specific subtopics.

Academic integrity

The University takes academic misconduct very seriously and is committed to ensuring that so far as possible it is detected and dealt with appropriately. Find out more about the University’s official policies around academic misconduct here.

Cheating or plagiarising on assignments, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the University policies, and will not be tolerated. Such incidences will result in a 0 (zero) grade for all parties involved. Additionally, there may be penalties to your final class grade along with being reported to the School Academic Misconduct Office.

Guidance on the use of Generative AI

To ensure all students have an equal opportunity to succeed and to preserve the integrity of the projects, students are not permitted to submit anything that is generated by artificial intelligence (AI) systems such as ChatGPT, Bing Chat, Claude, Google Bard, or any other automated assistance. This includes using AI to directly generate text for the report, code for the projects, or using AI to complete any other project tasks. Using AI in this way undermines your ability to develop critical thinking, writing, or research skills that are essential for the project and your academic success.

Students may use AI as part of their research and preparation for ideas, or judiciously as a text editor, but text that is submitted must be written by the student. For example, students may use AI to generate ideas, questions, or summaries that they then revise, expand, or cite properly. Students should also be aware of the potential benefits and limitations of using AI as a tool for learning and research. Note that any such use of generative AI must be explicitly acknowledged and a description of how it was used included as part of your submissions.

AI systems can provide helpful information or suggestions, but they are not always reliable or accurate. Students should critically evaluate the sources, methods, and outputs of AI systems. Violations of this policy will be treated as academic misconduct.

Please see the University’s Guidance on the use of Generative AI for further details.

Late work, Extensions, & Special Circumstances

All work is due on the stated due date. Due dates are there to help guide your pace through the course and they also allow us to return marks and feedback to you in a timely manner.

Extensions: For courses like AML involving a group coursework submission as part of the assessment, extensions are not allowed following Rule 2 of the School of Informatics’ Late Submission Rules and Penalties.
Special circumstances: If there is a documented reason why you’re unable to complete any assignment in the course, you might consider requesting a special circumstances waiver. Visit the Special Circumstances website and apply therein. Decisions are made at the end of the semester by an external committee.

If you’re unsure whether you apply for extensions or special circumstances, we recommend you reach out to your Student Advisor and/or Student Support Team (inf-sst@inf.ed.ac.uk).