CS 304 • Midterm • Introduction to Artificial Intelligence, Machine Learning and Data Science
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
Introduction to Machine Learning and AI
Formal Notation Explained
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
k-Nearest Neighbor (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
End-to-End Machine Learning Project
Feature Selection vs Feature Extraction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Evaluation/Performance Metrics
Loss Functions: Measuring Mistakes
Classification
Why Supervised Learning - MNIST Example
Maximum Likelihood Estimation(MLE)
Bayes' Theorem
Parametric Classification
Gaussian Classification Boundary
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Performance Measures
Nonparametric Classification
Training Models
Why Supervised Learning?
Hypothesis Space & Occam's Razor
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)
Linear Discriminant with Equal Variance
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Naive Density Estimator (Bandwidth effect & validity)
Sample Midterm Questions II
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Max-shift for SoftMax
Why Initialize Weights Near Zero?
Adaptive Learning Rates in Gradient Descent
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Naive Bayes Text Classification with Binary Features
Derivative of Softmax
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 and AI
Formal Notation Explained
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
k-Nearest Neighbor (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
End-to-End Machine Learning Project
Feature Selection vs Feature Extraction
Principal Component Analysis (PCA)
Feature Embedding & Factor Analysis (FA)
Evaluation/Performance Metrics
Loss Functions: Measuring Mistakes
Classification
Why Supervised Learning - MNIST Example
Maximum Likelihood Estimation(MLE)
Bayes' Theorem
Parametric Classification
Gaussian Classification Boundary
Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes
Multivariate Classification: Linear, Quadratic, and Model Selection
Performance Measures
Nonparametric Classification
Training Models
Why Supervised Learning?
Hypothesis Space & Occam's Razor
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)
Linear Discriminant with Equal Variance
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Naive Density Estimator (Bandwidth effect & validity)
Sample Midterm Questions II
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Max-shift for SoftMax
Why Initialize Weights Near Zero?
Adaptive Learning Rates in Gradient Descent
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Naive Bayes Text Classification with Binary Features
Derivative of Softmax