Study Guide

Objective

- Knowledge and skills to understand beyond-basic contemporary machine learning (ML) models and methods, and to choose and apply them in a principled and sound way
- Abilities for understanding connections to, and dependencies between, model/method properties and timely topics (e.g., ethics, sustainability, explainability).
- Abilities to solve a computational problem via machine learning without using a high-level ML application programming interface

Content

- Theory and practice of the beyond-basic contemporary machine learning (ML) models and methods
- Use of suitable tools (e.g., an ML application programming interface enabling both high and low level expression) for building solutions

Qualifications

Introduction to Machine Learning Methods

Assessment criteria, satisfactory (1)

Grade 1: The student knows the theory on the considered ML models and methods. The student is able to solve beyond-basic contemporary ML problems, using the considered tools.

Assessment criteria, good (3)

Grade 3: The student understand the theory on the considered ML models and methods. The student is able to solve a variety of beyond-basic contemporary ML problems, using the considered tools, suitably.

Assessment criteria, excellent (5)

Grade 5: The student understand the theory on the considered ML models and methods. The student is able to solve a variety of beyond-basic contemporary ML problems, using the considered tools, most suitably.