CS 454 • Final • 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 Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
Konular
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
Non-parametric Methods
What Nonparametric Means
Density Estimation
Nonparametric Classification
Condensed Nearest Neighbor
Outlier Detection
Nonparametric Regression
Additive Models & How to chose h/k
Decision Trees
What is a Decision Tree?
Splitting in Classification Trees
Pruning Trees
From Trees to Rules
Multivariate/Oblique Trees
Linear Discrimination
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
Multilayer Perceptrons
Perceptron
Training a Perceptron
Limitation: XOR
MLP Architecture & Representation View
Backpropagation
Regression
Discrimination
Deep Learning
Introduction to Deep Learning & Activation Functions
Training Deep Networks
Regularization Techniques
Tuning Network Structure
Learning Time
Time-Delay Neural Networks (TDNN)
RNN / LSTM / GRU
Generative Adversarial Networks (GANs)
Combining Multiple Learners
Why Combine Multiple Learners?
Voting & Linear Combination
Bayesian Perspective & Effect of Dependence
Fixed Combination Rules & ECOC
Bagging & AdaBoost
Mixture of Experts and Stacking
Fine-Tuning an Ensemble
Cascading
Combining Multiple Sources/Views
Sample Final Questions
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Linear Discriminant with Equal Variance
MLP with Hard-Threshold Units
Should we initialize all MLP weights to zero?
One Shared Network vs. Three Separate Networks
Naive Histogram Estimator vs. Parzen Windows (Kernel)
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
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
Non-parametric Methods
What Nonparametric Means
Density Estimation
Nonparametric Classification
Condensed Nearest Neighbor
Outlier Detection
Nonparametric Regression
Additive Models & How to chose h/k
Decision Trees
What is a Decision Tree?
Splitting in Classification Trees
Pruning Trees
From Trees to Rules
Multivariate/Oblique Trees
Linear Discrimination
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
Multilayer Perceptrons
Perceptron
Training a Perceptron
Limitation: XOR
MLP Architecture & Representation View
Backpropagation
Regression
Discrimination
Deep Learning
Introduction to Deep Learning & Activation Functions
Training Deep Networks
Regularization Techniques
Tuning Network Structure
Learning Time
Time-Delay Neural Networks (TDNN)
RNN / LSTM / GRU
Generative Adversarial Networks (GANs)
Combining Multiple Learners
Why Combine Multiple Learners?
Voting & Linear Combination
Bayesian Perspective & Effect of Dependence
Fixed Combination Rules & ECOC
Bagging & AdaBoost
Mixture of Experts and Stacking
Fine-Tuning an Ensemble
Cascading
Combining Multiple Sources/Views
Sample Final Questions
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Linear Discriminant with Equal Variance
MLP with Hard-Threshold Units
Should we initialize all MLP weights to zero?
One Shared Network vs. Three Separate Networks
Naive Histogram Estimator vs. Parzen Windows (Kernel)