Opinto-opas

Tavoitteet

Machine Learning has found its way into many of the services we use daily, e.g., Google Search, YouTube, Netflix, and Spotify. It is an application of artificial intelligence (AI) that deals with the challenge of computers performing tasks without being explicitly programmed. This course will introduce the student to the basic principles and concepts of machine learning. Apart from the intuitions, the student will get familiar with the most popular machine learning algorithms, their applications, and their intuitions. By the end of this course, the student will be prepared to enter the fantastic world of machine learning towards amazing job positions in the industry.

Sisältö

1. Introduction:
What is Machine Learning? – Machine Learning and Data – History and Philosophy – Python

2. Types of Machine Learning:
Supervised Learning – Classification – Regression – Unsupervised Learning – Clustering – Dimensionality Reduction – Reinforcement Learning

3. Regression:
Linear Regression – K-Nearest Neighbors Regression

4. Classification:
Logistic Regression – Linear Support Vector Machines – Kernelized Support Vector Machines – K-Nearest Neighbors Classification – Decision Tree – Neural Networks

5. Unsupervised Learning:
Curse of Dimensionality – Dimensionality Reduction with PCA – k-Means Clustering

6. Data Preprocessing:
Why Preprocessing? – Data Imputation – Feature Encoding – Feature Scaling

7. Model Development:
Model Generalization – Overfitting and Underfitting – Evaluation Metrics for Regression Models – Evaluation Metrics for Classification Models – Model Selection

8. Final Tasks
Final Project – Self-study Essay

Aika ja paikka

Course is 100% online (Self-Study) course and course environment is TechClass portal.

Oppimateriaalit

Lecture slides, tutorial videos, quizzes, exercises

Opetusmenetelmät

This course is 100% virtual, thanks to the comprehensive tutorial videos and content prepared for this course.

The student will pass this course after submitting the required quizzes, assignments, and the final project.

Harjoittelu- ja työelämäyhteistyö

N/A

Tenttien ajankohdat ja uusintamahdollisuudet

Online.

Kansainvälisyys

N/A

Toteutuksen valinnaiset suoritustavat

N/A

Opiskelijan ajankäyttö ja kuormitus

Lectures = 25h
Exercises = 10h
Self-study = 10h
Quizzes = 5h
Project = 10h
Total = 60 hours

Arviointiasteikko

Hyväksytty/Hylätty

Arviointikriteerit, tyydyttävä (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 AI and Machine Learning.
- The student knows the important real-life applications of Machine Learning.

Arviointikriteerit, hyvä (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.
- The student is familiar with neural networks.

Arviointikriteerit, kiitettävä (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 neural networks.
- 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.

Arviointimenetelmät ja arvioinnin perusteet

Exercise 20%
Quiz 30%
Project 30%
Essay 20%

Lisätiedot

Course is only for Diploma in Machine & Deep Learning students.