AI and Machine LearningLaajuus (8 cr)

Objectives

The student understands basic concepts of AI and Machine Learning. The student is able to create and use Machine Learning Algorithms in Python. The student learns how to make analysis and predictions and knows which Machine Learning model to choose for each type of a problem.

Content

- Basic concepts of AI and Machine Learning
- Unsupervised and Supervised learning
- Regression, Association, Classification
- Naïve Bayes, Decision Trees and Neural Network Algorithms
- Training and validation of models
- Production testing of models

Prerequisites

Basic knowledge of programming

Assessment criteria, satisfactory (1-2)

Student knows about the basic concepts of AI and Machine Learning. Student can apply at least some supervised or supervised learning applications. Student can use regression, association or classification algorithm with support. Student can create an application using either Naïve Bayes, Decision Trees or Neural Network Algorithms. Student can setup training and validation processes for new models. Student can setup production testing for new models.

Assessment criteria, good (3-4)

Student knows and understands the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning applications. Student can create applications with regression, association, or classification algorithms. Student can create working applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can setup and apply training and use validation methods for new models. Student can follow procedures of production testing for new models.

Assessment criteria, excellent (5)

Student knows and understands in depth the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning for various applications. Student can use regression, association, and classification algorithms where appropriate. Student can create versatile applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can implement various training and validation solutions for new models. Student is able to execute reliable production testing for new models.

Further information

Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.