CMPE 468 • Tüm Sınavlar • Machine Learning for Engineers
Atılım Üniversitesi CMPE 468 (Machine Learning for Engineers) Midterm sınavına hazırlık paketi.
İşlenen konular: Supervised Learning: Classification and Regression, kNN, Linear Models, Naive Bayes Classifiers, Decision Trees.
Ayda 833 TL, peşin fiyatına 3 taksit
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
2 konu anlatımı
Introduction to Machine Learning
Machine Learning Notation Explained
Probability Review
11 konu anlatımı
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
Supervised Learning: Classification and Regression
9 konu anlatımı
Why Supervised?
Hypothesis Space & Occam's Razor
Example: Least-Squares Linear Regression
What Is Regression?
Linear Regression
Multiple Linear Regression
Polynomial Regression
Summary: Linear, Multiple & Polynomial Regression
Feature Transformations & Feature Engineering
kNN
9 konu anlatımı · 1 soru
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
Linear Models
10 konu anlatımı
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
Naive Bayes Classifiers
8 konu anlatımı
Naive Bayes Approach
Curse of Dimensionality
Bayes Classifier vs. Naive Bayes
Independence & Conditional Independence
Naive Bayes Classification
How Naive Bayes Simplifies Parameter Estimation
Example with categorical variables
Naive Bayes Subtleties
Decision Trees
5 konu anlatımı
What is a Decision Tree?
Splitting in Classification Trees
Pruning Trees
From Trees to Rules
Multivariate/Oblique Trees
Sample Midterm Questions I
12 soru
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)
Pass Rates & Majors (Bayes; Law of Total Probability)
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?
Discrete Attribute in Decision Trees
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Choosing Between Two Splits: Gini vs. Misclassification
Naive Bayes Text Classification with Binary Features
Sample Midterm Questions II
7 soru
Mean Square Error for Linear Regression
k-NN Regression Prediction
Decision Boundary and Building a Network for Binary Classification
Derivative of Squared Error
True/False Reasoning on Activation, Linear Networks, and Gradient Descent
Computing Total Probability
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity
Clustering: K-Means
5 konu anlatımı · 3 soru
Introduction and Mixture Densities
K-Means Clustering
One Iteration of k-Means
Expectation-Maximization (EM)
Mixture Models & Practical Use of Clusters
Spectral and Hierarchical Clustering
Choose the Right Clustering Tool
“Clustering as Preprocessing” Pitfall
Sample Final Questions I
20 soru
Pass Rates & Majors (Bayes; Law of Total Probability)
Linear Discriminant with Equal Variance
Comparing Two Splits (Gini vs. Misclassification)
Prepruning vs. Postpruning (Which and Why?)
Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)
Mean Square Error for Linear Regression
Gradient Descent Update
k-NN Regression Prediction
Decision Boundary and Building a Network for Binary Classification
Derivative of Squared Error
Computing Input and Output of a Convolution Node
True/False Reasoning on Activation, Linear Networks, and Gradient Descent
Computing Total Probability
True/False on Scaling, k-NN, Intrinsic Error and Model Complexity
Regression: Test-Set MSE
Generalization & Overfitting: True/False
Baseline Error: ZeroR vs Random Guessing
Entropy: Fair Die & Bias Effect
Decision Trees: ID3 Optimality + Key Advantage
Decision Tree Split: Remaining Entropy
Sample Final Questions II
10 soru
Discrete Attribute in Decision Trees
From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier
Why Not Regression for Classification?
Adaptive Learning Rates in Gradient Descent
Mahalanobis vs. Euclidean: Why and When?
Regularized Least Squares
Gaussian Generative Model → Logistic Posterior
Why Initialize Weights Near Zero?
Naive Bayes Text Classification with Binary Features
Choosing Between Two Splits: Gini vs. Misclassification