Introduction to basic machine learning concepts and algorithms. Bayesian decision theory. Decision trees. Artificial neural networks. Evaluation of classification algorithms. Unsupervised learning and clustering. Reinforcement learning. Genetic algorithms. Recent topics in machine learning. Ensemble learning. Cost-sensitive learning. Active learning. Deep learning.
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| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 2.81 | 1 sec · 20 öğr |
| 2024-2025 Spring | 3.34 | 1 sec · 37 öğr |
| 2022-2023 Spring | 3.24 | 1 sec · 20 öğr |
| 2021-2022 Spring | 3.23 | 1 sec · 23 öğr |
| 2020-2021 Fall | 3.05 | 1 sec · 46 öğr |
| 2019-2020 Fall | 3.12 | 1 sec · 22 öğr |
| 2018-2019 Fall | 3.45 | 1 sec · 31 öğr |
| 2018-2019 Spring | 3.02 | 1 sec · 15 öğr |
| 2017-2018 Spring | 3.38 | 1 sec · 41 öğr |
| 2016-2017 Spring | 3.24 | 1 sec · 35 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
Course Learning Outcomes: Course Learning Outcome Assessment Employ the known algorithms to solve given problems Homework Propose and design new systems, by extending the known algorithms, to meet the given requirements Homework Midterm: Essay/Written Analyze and discuss experimental results Homework Project Use software tools Homework Apply knowledge of mathematics Homework Identify methods to meet the desired needs Midterm: Essay/Written Design and implement a system to find a solution to a re
We follow the Generative AI policy guideline of Bilkent University which can be found here: https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
Introduction Bayesian decision theory, Algorithm independent issues Decision trees Decision trees, Artificial neural networks Artificial neural networks Artificial neural networks, Deep learning Deep learning Unsupervised learning and clustering Unsupervised learning and clustering, Genetic algorithms Ensemble learning Kernel methods Reinforcement learning Presentations Presentations ECTS - Workload Table: Activities Number Hours Workload Course hours 14 3 42 Homework 3 15 45 Individual or group work 14 1 14 Report (including preparation and presentation if applicable) 1 10 10 Presentation (including preparation) 1 5 5 Preparation for Midterm exam 1 15 15 Project (including preparation and presentation if applicable) 1 20 20 Midterm exam 1 2 2 Total Workload: 153 Total Workload / 30: 153 / 30 5.1 ECTS Credits of the Course: 5 Type of Course: Lecture - Project Course Material: PP - Written Teaching Methods: Lecture - Presentations - Assignment