Παρουσίαση/Προβολή

(DIT187) -  Anastasia Krithara

Περιγραφή Μαθήματος

General Information

• When: Every Monday @ 18:00 (GMT+3)
• Slides to be uploaded on eClass

Instructor

• Anastasia Krithara (akrithara@iit.demokritos.gr)

Book

• Book not required - slides describe all you need (and they include references to additional material)

Marking scheme

• Exercises; 50% [2 mini-projects]
• Final exam (oral): 50% 
• To pass the course: average grade >6

Lectures

• 01 - Introduction 
• 02 - Decision trees 
• 03 - Linear Regression 
• 04 - Logistic Regression 
• 05 - kNN_Clustering & Evaluation metrics
• 06 - Applied Machine Learning
• 07 - Support Vector Machines 
• 08 - Ensembles 
• 09 - Feature Selection 
• 10 - Naive Bayes 
• 11 - Sampling 
• 12 - Advice
• 13 - Projects presentation by the students 

Exams

• To be announced

Ημερομηνία δημιουργίας

Πέμπτη 22 Οκτωβρίου 2020