AIN 3001 • Midterm • Machine Learning
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.
Konular
Introduction and Basics of Machine Learning
Introduction to Machine Learning
Machine Learning Notation Explained
General Flowchart of ML Models
Parameter vs Hyperparameter
Data Splitting
K-Fold Cross Validation
Train–Validation–Test Split
Generalization
Underfitting & Overfitting
Bias Variance Perspective
Evaluation
Closing Checklist
Bias–Variance & Model Complexity
Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Example: Least-Squares Linear Regression
Clustering
Introduction and Mixture Densities
K-Means Clustering
K-Means Iteration Calculation
Expectation-Maximization (EM)
Mixture Models & Practical Use of Clusters
Spectral and Hierarchical Clustering
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis (PCA)
Covariance Matrix Calculation
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
SVM
What & Why
Maximum Margin Classification
Maximizing the Margin
Lagrangian Formulation of the Hard-Margin SVM
From Primal to Dual: Solving the SVM Optimization
Why only a few points matter (KKT & sparsity)
From 𝛼 to parameters
Prediction uses only support vectors
Soft Margin SVM
Soft Margin Dual
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
Sample Midterm Questions
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?
Feature Mapping & Transformation
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity
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 and Basics of Machine Learning
Introduction to Machine Learning
Machine Learning Notation Explained
General Flowchart of ML Models
Parameter vs Hyperparameter
Data Splitting
K-Fold Cross Validation
Train–Validation–Test Split
Generalization
Underfitting & Overfitting
Bias Variance Perspective
Evaluation
Closing Checklist
Bias–Variance & Model Complexity
Supervised Learning
Why Supervised?
Hypothesis Space & Occam's Razor
Loss Functions: Measuring Mistakes
Example: Least-Squares Linear Regression
Clustering
Introduction and Mixture Densities
K-Means Clustering
K-Means Iteration Calculation
Expectation-Maximization (EM)
Mixture Models & Practical Use of Clusters
Spectral and Hierarchical Clustering
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis (PCA)
Covariance Matrix Calculation
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
SVM
What & Why
Maximum Margin Classification
Maximizing the Margin
Lagrangian Formulation of the Hard-Margin SVM
From Primal to Dual: Solving the SVM Optimization
Why only a few points matter (KKT & sparsity)
From 𝛼 to parameters
Prediction uses only support vectors
Soft Margin SVM
Soft Margin Dual
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
Sample Midterm Questions
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?
Feature Mapping & Transformation
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity