Machine learning and Artificial Intelligence 2 (5cr)

Evaluation scale

H-5

Objective

This module is covering advanced topics of Machine Learning and Artificial Intelligence methods and algorithms used in Data Analytics. In addition to theoretical knowledge on these methods, you will learn how to implement them in a programming environment. The prerequisite for this course is a successful completion of Mathematics and Statistics 1 and 2, Machine learning and Artificial Intelligence 1, and a good understanding of Data Analytics processes and methods.

Content

Tieto puuttuu

Location and time

30.8.2021 - 17.9.2021

Materials

To be announced in the beginning of the course

Teaching methods

Problem-based and team based learning may be applied where applicable. Students will seek information and solve problems related to subject presented. Different activating vocational teaching methods will be used depending on the group taught and the facilities available. If applicable, conventions from selected areas in software industry may be used as a part of teaching. Teacher guides the learning process by short introductory lectures and/or initial subject related material to be studied before practical work. Teacher prepares the setting for learning and provides coaching for the students. Teaching sessions may take place on campus and online. The main focus will be on guided knowledge searching and practical work on it.

Employer connections

Software industry conventions are used. This course may include a case company selected by the university. In addition, students are able to propose your own case companies, whose business information and data analytics they would like to develop. Students must provide a free form commission agreement from their own case companies.

Completion alternatives

Before the course starts, students may propose to the course teachers their personal implementation plan. The plan must be realistic and result in verificable development in the targeted competence(s). In addition, guidance from MIGRI and student visa must be taken into account. Course teachers accept or reject student's plan based on their own consideration.

Student workload

The student's estimated workload of this implementation is 135 h as follows:
Roughly half is Independent individual and teamwork guided when needed.
Rest of hours will be mostly learning the subject with guided practical and knowledge seeking exercises.

Assessment criteria, satisfactory (1)

Evaluation target: You know the essentials of Machine Learning (ML) algorithms and Artificial Intelligence (AI) and can apply them in practice.

Satisfactory
You implement simple ML and AI tasks in a programming environment, but you often need assistance and instructions.

Assessment criteria, good (3)

Good
You implement typical ML and AI tasks according to the requirements, and you can solve related challenges independently.

Assessment criteria, excellent (5)

Excellent
You produce complex and efficient implementations of ML and AI tasks independently and according to the requirements.

Qualifications

NULL