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ME 405

Machine Learning for Mechanical Engineering

ME 405 treats machine learning as a working tool for mechanical engineers, focused on turning experimental and simulation data, microscopy images, materials databases, atomistic descriptors, into models that predict structure-property relationships or drive a self-driving lab. You'll move through the full pipeline in Python: cleaning and curating data, fitting regression and classification models, then progressing to neural networks, Gaussian processes, and reinforcement learning, with two homeworks and a project where you apply this to a real ME problem. It assumes you're comfortable with programming and statistics, and it's the bridge between traditional ME coursework and the data-driven side of modern materials and mechanics research.

Kredi3ECTS5FakülteFaculty of EngineeringBölümMechanical EngineeringKoordinatörOrçun Koray Çelebi

Ön koşul(MATH 220 veya MATH 224 veya MATH 225 veya MATH 241) ve (MATH 230 veya MATH 255 veya MATH 260) ve (CS 102 veya CS 114 veya CS 115) ve (CHEM 201 veya PHYS 212)

→ STARS müfredatı (resmi syllabus)

Haftalık müfredat 14 hafta

Hafta 114–20 Eyl

Machine learning ve Python ile istatistik

Course outline, introduction to machine learning, data driven approach for mechanical engineering, algebra and statistics with Python

machine learningdata driven approachalgebraPython

Hafta 221–27 Eyl

Makine öğrenmesi yöntemlerine genel bakış

Overview of supervised, unsupervised and reinforcement learning methods

supervised learningunsupervised learningreinforcement learning

Hafta 328 Eyl – 4 Eki

Deployment, rastgele sayılar ve veri yönetimi

Deployment, random numbers, data management

deploymentrandom numbersdata management

Hafta 45–11 Eki

Makine Mühendisliğinde Veri Türleri ve Curation

Data types in mechanical engineering, online data repositories, data analysis, cleanup, preparation, curation

data typesonline data repositoriesdata cleanupcuration

Hafta 512–18 Eki

Linear regression, validation ve model seçimi

Linear regression, validation, evaluation, feature engineering, descriptor selection, tuning and model selection

linear regressionfeature engineeringdescriptor selectionmodel selection

Hafta 619–25 Eki

Regression modelleri

Regression models

regression model

Hafta 726 Eki – 1 Kas

Classification modelleri

Classification models

classification models

Hafta 82–8 Kas

Materials database kullanımı

Usage of materials databases

materials database

Hafta 99–15 Kas

Neural networks (yapay sinir ağları)

Neural networks

neural networks

Hafta 1016–22 Kas

Deep learning (derin öğrenme)

Deep learning

deep learning

Hafta 1123–29 Kas

Atomistic machine learning

Hafta 1230 Kas – 6 Ara

Atomistic descriptor'lar ve materials ontology

Atomistic descriptors, materials ontologies

atomistic descriptorsmaterials ontologies

Hafta 137–13 Ara

Molecular dynamics için machine learning potentials

Machine learning potentials for molecular dynamics

machine learning potentialsmolecular dynamics

Hafta 1414–20 Ara

Reinforcement learning ve self-driving labs

Reinforcement learning, self-driving labs

reinforcement learningself-driving lab

Bu dersi alınca · 5 öğrenme çıktısı

Curate and manage data in databases, visualize data, transform it into descriptors compatible with machine learning modelsHomework Midterm Project Final
Utilize supervised, unsupervised, reinforcement learning methods on various data sets in mechanical engineeringHomework Midterm Project Final
Apply regression, classification, and deep learning models to solve mechanical engineering problemsHomework Midterm Project Final
Communicate their reasoning effectively to their peersHomework Project
Collaborate professionally and scientifically with their peersProject

Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.

🤖 GenAI politikası

Students are advised to consult their instructor regarding the use of Generative AI tools and their appropriateness. Responsible use of GenAI is encouraged in accordance with Bilkent University's GenAI Guidelines (https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline).

Ders notları · henüz yok

ME 405 için defter ekibi henüz not yazmadı.

İlk dosyayı sen atarsan: not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa. defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.

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Bu dönem · 2026-2027 Güz · 1 şube · 40 kontenjan

haftada 3 saat ders

Haftalık ızgarada 4 saat görünüyor, kayıt sistemi dersi 3 saat yazıyor. Aradaki 1 saat yedek saat: Bilkent programlarında derse genelde bir fazla saat ayrılır ve çoğu zaman kullanılmaz. Hangisinin düşeceği dönem başlayınca belli olur.

Orçun Koray Çelebi

Çar10:30–12:20Cum15:30–17:20

kişilik

programıma ekle → CGPA'ya ekle kayıt tarihleri

⚠️ FZ engelleyen şartlar

No FZ grade is given in this course

Hocalar 1 bu dönem · 0 geçmiş

Bu dönem (2026-2027 Fall) · 1 section

Orçun Koray Çelebi