CMPE 468 • Midterm • Machine Learning for Engineers
Dersi 3 kişi birlikte alın
Eğitmen
Nursena Köprücü Aslan
MSc in Machine Learning and AI
Koç Üniversitesi’nde Bilgisayar Mühendisliği okudum ve aynı zamanda Matematik alanında çift anadal yaptım. Ardından Imperial College London’da Machine Learning and Artificial Intelligence alanında yüksek lisansımı tamamladım. Bu süreçte yurtdışında farklı araştırma projelerinde yer aldım ve özellikle makine öğrenmesi, yapay zekâ ve veri bilimi konularında hem akademik hem de uygulamalı deneyim kazandım. Bu derste amacım, makine öğrenmesinin temel kavramlarını anlaşılır ve uygulamaya dönük bir şekilde sizlerle paylaşmak.
Paketi Tamamla
🎓 Atılım Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
CMPE 468 • Final
Machine Learning for Engineers
Nursena Köprücü Aslan
1499 TL
CMPE 468 • Midterm
Machine Learning for Engineers
Nursena Köprücü Aslan
1499 TL
Konular
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Probability Review
Counting and Probability
Conditional Probability and Independence
Bayes' Rule
Discrete Random Variables
Continuous Random Variables
Expected Value and Variance
Bernoulli and Binomial Distributions
Continuous Uniform Distribution
Exponential Distribution
Normal Distribution
Laplace and Logistic Distributions
Supervised Learning: Classification and Regression
Why Supervised?
Hypothesis Space & Occam's Razor
Example: Least-Squares Linear Regression
What Is Regression?
Linear Regression
Multiple Linear Regression
Polynomial Regression
Summary: Linear, Multiple & Polynomial Regression
Feature Transformations & Feature Engineering
kNN
Nearest Neighbor Approach
Value of k
Geometric View: Voronoi Intuition
Distance Measure
Distances for Real Vectors
Example: Computing Distance Between Two Points
Distance for Non-Numeric Data
Scaling and Normalization
Voting Mechanism
k-NN Regression
Linear Models
The Problem
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Logistic Discrimination: Two Classes
Logistic Discrimination: K>2 Classes
Generalizing the Linear Model
Discrimination by Regression
Learning to Rank
Naive Bayes Classifiers
Naive Bayes Approach
Curse of Dimensionality
Bayes Classifier vs. Naive Bayes
Independence & Conditional Independence
Naive Bayes Classification
How Naive Bayes Simplifies Parameter Estimation
Example with categorical variables
Naive Bayes Subtleties
Decision Trees
What is a Decision Tree?
Splitting in Classification Trees
Pruning Trees
From Trees to Rules
Multivariate/Oblique Trees
Sample Midterm Questions I
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)
Pass Rates & Majors (Bayes; Law of Total Probability)
Why Not Regression for Classification?
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Mahalanobis vs. Euclidean: Why and When?
Discrete Attribute in Decision Trees
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Choosing Between Two Splits: Gini vs. Misclassification
Naive Bayes Text Classification with Binary Features
Sample Midterm Questions II
Mean Square Error for Linear Regression
k-NN Regression Prediction
Decision Boundary and Building a Network for Binary Classification
Derivative of Squared Error
True/False Reasoning on Activation, Linear Networks, and Gradient Descent
Computing Total Probability
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Probability Review
Counting and Probability
Conditional Probability and Independence
Bayes' Rule
Discrete Random Variables
Continuous Random Variables
Expected Value and Variance
Bernoulli and Binomial Distributions
Continuous Uniform Distribution
Exponential Distribution
Normal Distribution
Laplace and Logistic Distributions
Supervised Learning: Classification and Regression
Why Supervised?
Hypothesis Space & Occam's Razor
Example: Least-Squares Linear Regression
What Is Regression?
Linear Regression
Multiple Linear Regression
Polynomial Regression
Summary: Linear, Multiple & Polynomial Regression
Feature Transformations & Feature Engineering
kNN
Nearest Neighbor Approach
Value of k
Geometric View: Voronoi Intuition
Distance Measure
Distances for Real Vectors
Example: Computing Distance Between Two Points
Distance for Non-Numeric Data
Scaling and Normalization
Voting Mechanism
k-NN Regression
Linear Models
The Problem
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Logistic Discrimination: Two Classes
Logistic Discrimination: K>2 Classes
Generalizing the Linear Model
Discrimination by Regression
Learning to Rank
Naive Bayes Classifiers
Naive Bayes Approach
Curse of Dimensionality
Bayes Classifier vs. Naive Bayes
Independence & Conditional Independence
Naive Bayes Classification
How Naive Bayes Simplifies Parameter Estimation
Example with categorical variables
Naive Bayes Subtleties
Decision Trees
What is a Decision Tree?
Splitting in Classification Trees
Pruning Trees
From Trees to Rules
Multivariate/Oblique Trees
Sample Midterm Questions I
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)
Pass Rates & Majors (Bayes; Law of Total Probability)
Why Not Regression for Classification?
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Mahalanobis vs. Euclidean: Why and When?
Discrete Attribute in Decision Trees
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Choosing Between Two Splits: Gini vs. Misclassification
Naive Bayes Text Classification with Binary Features
Sample Midterm Questions II
Mean Square Error for Linear Regression
k-NN Regression Prediction
Decision Boundary and Building a Network for Binary Classification
Derivative of Squared Error
True/False Reasoning on Activation, Linear Networks, and Gradient Descent
Computing Total Probability
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity
Sıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.