ENGR 421 • Midterm I • 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
🎓 Koç Üniversitesinde öğrencilerin %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
Sample Mean and Covariance Matrix
Mahalanobis Distance
LDA vs QDA Classification
Naive Bayes Classification (Discrete Features)
Linear Discrimination
The Problem
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Gradient Descent Update
Linear Regression + MSE: Gradient Descent Step Size Effects
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
Should we initialize all MLP weights to zero?
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)
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
MLP with Hard-Threshold Units
One Shared Network vs. Three Separate Networks
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Kernel Smoother
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
Why Initialize Weights Near Zero?
Adaptive Learning Rates in Gradient Descent
When Do Direct Input Output Links Help in an MLP?
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
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
Sample Mean and Covariance Matrix
Mahalanobis Distance
LDA vs QDA Classification
Naive Bayes Classification (Discrete Features)
Linear Discrimination
The Problem
Linear Discriminant
Two Classes/Multiple Classes/Pairwise Seperation
From Discriminants to Posteriors
Gradient Descent
Gradient Descent Update
Linear Regression + MSE: Gradient Descent Step Size Effects
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
Should we initialize all MLP weights to zero?
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)
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
MLP with Hard-Threshold Units
One Shared Network vs. Three Separate Networks
Naive Histogram Estimator vs. Parzen Windows (Kernel)
Kernel Smoother
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
Why Initialize Weights Near Zero?
Adaptive Learning Rates in Gradient Descent
When Do Direct Input Output Links Help in an MLP?
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Naive Bayes Text Classification with Binary Features
Derivative of Softmax
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.