CS 454 • Midterm • Introduction to Machine Learning and Artificial Neural Networks
Eğitmen
Nursena Köprücü Aslan
PhD in Computer Science
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. Şu anda University of Cambridge'te doktora çalışmalarımı sürdürüyorum.
Paketi Tamamla
🎓 Özyeğin Üniversitesindeöğrencilerin %92'si tüm paketi alarak çalışıyor.
Konular
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Machine Learning Preliminaries
Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Example: Least-Squares Linear Regression
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
Maximum Likelihood Estimation(MLE)
Bernoulli Likelihood
Multinomial Likelihood and Smoothing
Bayes' Theorem
Parametric Classification
Unequal Variances → Quadratic Boundary
Gaussian Classification Boundary
Parametric & Polynomial Regression
Multivariate Methods
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Discrete Features & Multivariate Regression
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Singular Value Decomposition and Matrix Factorization
Multidimensional Scaling
Linear Discriminant Analysis (LDA)
Canonical Correlation Analysis
Isomap, Locally Linear Embedding, Laplacian Eigenmaps
Clustering
Introduction and Mixture Densities
K-Means Clustering
Expectation-Maximization (EM)
Mixture Models & Practical Use of Clusters
Spectral and Hierarchical Clustering
Sample Midterm Questions
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)
Kernel Smoother
Naive Density Estimator (Bandwidth effect & validity)
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)
Değerlendirmeler
Henüz hiç değerlendirme yok.
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.
Ders İçeriği
Introduction
Introduction to Machine Learning
Machine Learning Notation Explained
Machine Learning Preliminaries
Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Example: Least-Squares Linear Regression
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
Maximum Likelihood Estimation(MLE)
Bernoulli Likelihood
Multinomial Likelihood and Smoothing
Bayes' Theorem
Parametric Classification
Unequal Variances → Quadratic Boundary
Gaussian Classification Boundary
Parametric & Polynomial Regression
Multivariate Methods
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Discrete Features & Multivariate Regression
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Singular Value Decomposition and Matrix Factorization
Multidimensional Scaling
Linear Discriminant Analysis (LDA)
Canonical Correlation Analysis
Isomap, Locally Linear Embedding, Laplacian Eigenmaps
Clustering
Introduction and Mixture Densities
K-Means Clustering
Expectation-Maximization (EM)
Mixture Models & Practical Use of Clusters
Spectral and Hierarchical Clustering
Sample Midterm Questions
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)
Kernel Smoother
Naive Density Estimator (Bandwidth effect & validity)
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)