CS 464 • Midterm • Introduction to Machine Learning
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
🎓 Bilkent Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
CS 464 • Final
Introduction to Machine Learning
Nursena Köprücü Aslan
1799 TL
CS 464 • Midterm
Introduction to Machine Learning
Nursena Köprücü Aslan
1799 TL
Konular
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Machine Learning Preliminaries
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
Parametric Methods and Estimation
Maximum Likelihood Estimation(MLE)
Bernoulli Likelihood
Multinomial Likelihood and Smoothing
Bayes' Theorem
Parametric Classification
Unequal Variances → Quadratic Boundary
Gaussian Classification Boundary
Multivariate Data and Naive Bayes
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Discrete Features & Multivariate Regression
Sample Mean and Covariance Matrix
Mahalanobis Distance
LDA vs QDA Classification
Naive Bayes Classification (Discrete Features)
Feature Selection, Feature Extraction and Performance Metrics
Feature Selection vs Feature Extraction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Evaluation/Performance Metrics
Linear Regression and Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Linear & Polynomial Regression
Example: Least-Squares Linear Regression
The Problem in Linear Regression
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Sample Midterm Questions I
Pass Rates & Majors (Bayes; Law of Total Probability)
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
MLE for α (positive support, exponential tail)
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Naive Density Estimator (Bandwidth effect & validity)
Sample Midterm Questions II
Why Not Regression for Classification?
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Max-shift for SoftMax
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Naive Bayes Text Classification with Binary Features
Derivative of Softmax
Kernel Density Estimation
Past Exam Questions
MLE for a Discrete PMF
1-NN LOOCV on Patient Dataset
Linear Regression + MSE: Gradient Descent Step Size Effects
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Machine Learning Preliminaries
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
Parametric Methods and Estimation
Maximum Likelihood Estimation(MLE)
Bernoulli Likelihood
Multinomial Likelihood and Smoothing
Bayes' Theorem
Parametric Classification
Unequal Variances → Quadratic Boundary
Gaussian Classification Boundary
Multivariate Data and Naive Bayes
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Discrete Features & Multivariate Regression
Sample Mean and Covariance Matrix
Mahalanobis Distance
LDA vs QDA Classification
Naive Bayes Classification (Discrete Features)
Feature Selection, Feature Extraction and Performance Metrics
Feature Selection vs Feature Extraction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Evaluation/Performance Metrics
Linear Regression and Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Linear & Polynomial Regression
Example: Least-Squares Linear Regression
The Problem in Linear Regression
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Sample Midterm Questions I
Pass Rates & Majors (Bayes; Law of Total Probability)
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
MLE for α (positive support, exponential tail)
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Naive Density Estimator (Bandwidth effect & validity)
Sample Midterm Questions II
Why Not Regression for Classification?
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Max-shift for SoftMax
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Naive Bayes Text Classification with Binary Features
Derivative of Softmax
Kernel Density Estimation
Past Exam Questions
MLE for a Discrete PMF
1-NN LOOCV on Patient Dataset
Linear Regression + MSE: Gradient Descent Step Size Effects
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.