Information Package
baskent.edu.tr

Program of Artificial Intelligence Engineering (English)

AI313

INTRODUCTION TO MACHINE LEARNING

Course Code
AI313
Semester
Fifth Term (Fall)
Theory+Practice
3 + 1
ECTS
5
Course TypeCompulsory Course
Course LevelBachelor's Degree
Year of Study3
SemesterFifth Term (Fall)
ECTS5
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Know hypotheses and version spaces.
  2. Know how to design a learning system.
  3. Know how to apply machine learning techniques to specific problems.
Mode of DeliveryFace to face
PrerequisitesYes(AI221)
Recommended Optional Programme Components
Course DescriptionMachine learning paradigms. Concept learning, supervised and unsupervised learning, general-to-specific ordering and version spaces. Decision tree learning. Artificial neural networks, perceptrons, and multilayer networks. Bayesian learning. Instance-based learning. Support vector machine. Genetic algorithms. Applications for selected problems.
Course Contents
WEEK TOPICS
1st Week Machine learning paradigms.
2nd Week Concept learning, general-to-specific ordering and version spaces.
3rd Week Concept learning, general-to-specific ordering and version spaces.
4th Week Decision tree learning.
5th Week Decision tree learning.
6th Week Artificial neural networks, perceptrons, and multilayer networks.
7th Week Artificial neural networks, perceptrons, and multilayer networks.
8th Week Mid-term
9th Week Bayesian learning.
10th Week Instance-based learning.
11th Week Support vector machine.
12th Week Genetic algorithms.
13th Week Genetic algorithms.
14th Week Applications to selected problems.
Recommended or Required Reading* Machine Learning, Tom Mitchell, McGraw-Hill.
* Pattern Recognition and Machine Learning, Christopher Bishop, Springer.
* Pattern Classification, Richard O. Duda, Wiley.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Questions/Answers
  • Practice
  • Problem Solving
  • Project
  • Report Preparation
  • Presentation
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Quiz 1 15
Project 1 15
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
Language of Instruction-
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12