Fundamentals of Machine Learning (5 cr)

Objective

This is an introductory course which provides a unique opportunity for the student to be familiar with the basic concepts of Machine Learning. After passing this course, the student will be able to understand different types of Machine Learning algorithms as well as the intuitions behind them. Furthermore, real-world applications of the methods are given throughout the course to prepare the student’s mind for future academic and professional opportunities.

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

The student will pass this course after submitting required quiz and assignments.

Content

1. Introduction:
Why Machine Learning? – Types of Machine Learning – History and Philosophy – Why Python?

2. Supervised Learning:
Classification vs. Regression – Linear Regression – Logistic Regression – Linear Support Vector Machines – Kernelized Support Vector Machines – K-Nearest Neighbors – Decision Tree

3. Data Preprocessing
Why Preprocessing? – Data Imputation – Feature Encoding – Feature Scaling

4. Unsupervised Learning:
Dimensionality Reduction – Clustering

5. Model Development:
Model Selection - Model Evaluation

Location and time

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

Materials

Online.

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

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

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.

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.

Assessment methods and criteria

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