Fundamentals of Machine Learning (5 ECTS)

Location and time

Course is online in TechClass portal and can be done in own pace.

Materials

Online.

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

Evaluation methods and criteria

Exercises 30%
Quizzes 20%
Project 40%
Essay 10%

Student workload

Lectures = 40h
Assignments = 25h
Self-study = 40h
Quiz = 5h
Project = 20h
Essay = 5h
Total = 135 hours

Content scheduling

Up to Student her-/himself.

Teaching methods

This course is 100% virtual thanks to the comprehensive interactive material and content prepared for this course.

Course includes:
- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, satisfactory (1)

- The student knows the basic definitions of Machine Learning.
- The student knows the general framework of Machine Learning algorithms.
- The student knows the primary types of Machine Learning methods.
- The student is familiar with the history of artificial intelligence and machine learning.
- The student knows the important real-life applications of Machine Learning.

Assessment criteria, good (3)

- The student knows the intuitions behind the linear regression and logistic regression.
- The student knows how K-NN method works.
- The student is familiar with the general concept of data preprocessing.
- The student is familiar with the curse of dimensionality and the need for dimensionality reduction.
- The student is familiar with the general framework of clustering algorithms.
- The student is familiar with the decision trees in general.

Assessment criteria, excellent (5)

- The student understands the intuition behind the linear and kernelized SVM.
- The student understands the intuition behind the decision tree classifier.
- The student understands the intuition behind preprocessing methods such as imputation, feature encoding, and feature scaling.
- The student knows the intuitions behind the data clustering using k-means method.
- The student knows the intuitions behind using PCA to represent data in lower dimensional space.
- The student knows the key concepts of model selection and model evaluation in Machine Learning
- The student knows when to use different model selection/evaluation criteria to compare different models and to choose the best among them.