Mathematical Principles of AI (5 cr)

Evaluation scale

H-5

Schedule

The course has weekly lessons according to the timetable. A detailed weekly timetable will be made available on the Moodle learning environment if the Director of Studies decides to starts this implementation. The timetable for the returnable learning assignments will follow the lecture timetable.

Implementation methods, demonstration and Work&Study

This course is organised as a CONTACT implementation type. Attendance in classes and exam 48 h, independent study 87 h. Total 135 hours.



The course includes Moodle quizzes and homework assignments, each of which must be returned in accordance with the timetable for successful completion. Finally, the exam must also be passed in the same implementation.



If one already knows the learning outcomes of this course, the skills according to the knowledge can be demonstrated. Ask your course teacher for more information. For more information on the recognition of competences: https://www.haaga-helia.fi/fi/osaamisen-tunnistaminen-ja-work-study

Intro

This course will introduce the participant in the mathematics used in applications of artificial intelligence and also required to develop AI applications. In particular, the course covers the essential elements of differential and integral calculus, linear algebra and numerical methods for optimisation calculations used in machine learning. Mathematical calculations of the course are done both by hand and using Python's symbolic and numerical libraries.

Materials

The learning material is mainly distributed through the Moodle learning environment. The course brings together key mathematical concepts related to the applications of AI. It also provides tools for applied analytics research and thesis work. Such issues are constantly evolving and the course will make use of material produced by the course teachers and otherwise up-to-date material.

Teaching methods and instruction

This course implementation has lessons every week. The lessons include relevant mathematical principles and the use of Python in calculations. Some of the lessons are intended to the guidance of course assignments. In particular, for each course assignment before the submission date there is an associated lecture.

The course implementation welcomes independent study of topics, e.g. by means of examples in the course material, within the framework of the Haaga-Helia University of Applied Sciences' guidelines.

No previous experience in coding is required.

Working life connections

The course implementations are designed for students who are about to enter work life or are already working there. The course content takes into account the content used in the field.

Exam dates and re-exam possibilities

The exam will be held at the end of the course on a date to be mutually agreed upon participants during the course. The date of the re-examination will also be agreed together during the implementation.

In all exams and retakes participant must be present in Pasila, where the identity of the examiner will be verified.

In the case of examinations, the participant has restricted access to some materials and coding environment. It is therefore not possible to conduct the test as in electron exam facilities (Exam).

Internationality

Mathematical and methodological skills are international skills.

Completion alternatives

There are no shortcuts to learning, and doing the work is essential. Other ways to get your grade in the Haaga-Helia University of Applied Sciences' register of credits should be inquired from the teacher responsible for the course.

Learning assignments

The topics presented in the course lectures are learned by doing related exercises. Assignments will be returned regularly throughout the course.

The learning tasks will practice the use of applications of mathematical concepts and their calculation using the Python programming language.

Assessment methods

The learning assignments account for 40% of the final grade and the exam for 60%.