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Showing posts with the label EM Algorithm

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Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes

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- September 04, 2024

Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs

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- September 04, 2024

WEEK 7: • Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.

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- October 26, 2023

WEEK 7: Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.

Labels

-Z-test
5
A CONCEPT LEARNING TASK
accuracy
Activation Function
Activation Functions
AdaBoost
Adding Similarity Features
ANALYTICAL LEARNING
AND Gate

and Reinforcement Learning
ANN
Applications of Machine learning
Appropriate Problems for Decision Tree Learning
Appropriate Problems for Neural Network Learning
ARTIFICIAL INTELLIGENCE
Artificial Neural Network
Artificial Neural Networks
Artificial Neuron
AUC
Autoencoders
Back Propagation
Backpropagation
Backward Pass
Backward Propagation
Bagging
Bagging and pasting
Batch and Online Learning
batch learning
Bayes Optimal Classifier
Bayes Theorem
Bayes Theorem and Concept Learning
Bayesian Belief Networks
Bayesian Learning
Bayesian Network
Binary
black Box
boosting
Building Block of Neural Networks
Business Acumen Rounds
C
Callback
Candidate elimination Algorithm
CART
CFG
Challenges
Challenges of machine learning
checkers game
Class
Classification
Classifier
clone
Cluster
Clustering
CNN
Computational Complexity
Concept
CONCEPT LEARNING
Concept Learning and the General to Specific Ordering
Concept Learning and the General to Specific Ordering
Concept Learning as Search
Concept Learning Task
conflict
Confusion Matrix
consistent
constraints
Convolution
Convolution Layer
Convolution Neural Network Layer
Correlation Analysis
Correlation Matrix
Cross-Entropy Loss Function
Crossover
CSV
CSV file
data augmentation
Data Preprocessing
Data Wrangling
DBSCAN
decision boundary
Decision function and Predictions
Decision Tree
Decision Tree Characteristics
Decision Tree Learning
Decision Tree Program
Decision Tree Representation
Decision Trees
deep autoencoders
Deep Learning
Deep Learning Fundamentals
Defining Success
definition of machine learning
Degree for Polynomial
Denoising autoencoders
dense layer
DESIGNING A LEARNING SYSTEM
Determining Optimal Gamma
DFA
Dictionary
Difficulties in Convergence
Dimensionality Reduction
Diving into Transfer Learning
DL Performance
driverless car
Dropout
DVCS
EM Algorithm
Enjoy Sport
Ensemble
Ensemble Learning and Random Forests
Ensemble Learning MCQs
Ensemble Models
Entropy
Equivalence of DFA & NFA
Essential Elements
Estimating Class Probabilities
Euclidean distance
Examples of Machine Learning Applications
Excel
experience
Explanation-Based Learning of Search Control Knowledge
F1 score
Factors
False Negative Rate
False Positive Rate
FC
Feature Count
File
Find S
Find-S
FIND-S Algorithm
Find-S: Algorithm
finding similarities
Fitness
FLAT
Flatten
forking
Forward
Forward Pass
Forward Propagation
Fully Connected Layer
Functional Model
GANs
Gaussian RBF Kernel
general concepts
general hypothesis
general-to-specific ordering of hypothesis
Generalizing from Examples
Genetic Algorithm MCQs
Genetic Algorithms
Genetic Operators
Genetic Programming
Gini
Gini Impurity or Entropy?
Gini Index
GIT
GITHUB
gradient
Gradient Boosting
GRU
ham
Handling Branches
handwriting recognition learning problem.
Handwritten digit
Handwritten Digit classification
Hard Margin
Hard Voting
Heart Disease Dataset
Hierarchical Clustering
Hinge Loss
How does clustering work
How Machine Learning Works? Machine Learning
Hyperparameter
Hypotheses
hypothesis
hypothesis space
ID3
ID3 Algorithm
ILP
image
Importance of Defining Success
Impurity
Indent level 4
Information Gain
Instance
instance based learning
instance space
INSTANCE-BASED LEARNING
Instance-Based Versus Model-Based Learning
instances
Insufficient Quantity of Training Data
Internal Factor
Intro to Hypothesis Testing
Introduction
Introduction to Supervised
Iris
IRIS dataset
Irrelevant Features
Issues In Decision Tree Learning
JNTUA
JNTUH
JNTUK
JSON
K-Fold Cross Validation
k-Means Algorithm
k-Means Clustering
K-Nearest Neighbor Algorithm
k-Nearest Neighbors Algorithms
Keras
Kernel
Kernelized SVM Online SVMs
Key Performance Indicator
KNN
KNN Algorithm
KPI for Consumer Service
Label 5
Layers
LeakyReLU
Learning
Learning First-Order Rules
Learning Rate
Learning Rate Decay
Learning Rule Sets: Summary
Learning Sets of First-Order Rules: FOIL
Learning Sets of Rules
Learning With Perfect Domain Theories: PROLOG-EBG
Linear classifier
Linear Regression
Linear SVM Classification
LinearSVM
List of Strings
list-then-elimination algorithm
Literal
Local and Spurious Optima
Locally Weighted Regression
Logistic Regression
Long Answer Questions
Loss
LSTM
Machine Learning
machine learning approach
Machine Learning Landscape
Machine Learning MCQs
Machine Learning Programs
machine learning. spam detection
Main Approaches for Dimensionality Reduction
Main Challenges of Machine Learning
Making Predictions
Max Pooling Layer
MCQs
Measuring Accuracy Using Cross-Validation
merge
Methodologies
Metric Change
Metric for Finance
Missing Values
MLOPs
MLP
MLPs
MNIST
MNIST Dataset
model
model based learning
Models
Models of Evolution and Learning
Momentum
more general than hypothesis
Most Specific Hypothesis
Motivation
Multiclass
Multilabel
Multilayer Networks
Mutation
Naive Bayes
Naive Bayes Algorithm
Naive Bayes Classifier
Naïve Bayes Classifier
Naive Bayes Classifier - Example -classify- play tennis - forecast
Navie Bayes
Need for NN
Negative Training Example
Neural Network
Neural Network MCQs
Neural Network Representation
Neural Networks
Neuron
NFA
Nodes
non-parametric Locally Weighted Regression Algorithm
Non-Representative
Nondeterministic Rewards and Actions
Nonlinear SVM Classification
Nonrepresentative Training Data
normalizing features
North Star for Companies
North Star for Instagram
Not 5
online learning
optimizer
OvA
Overfitting
OvO
Padding
Parallelizing Genetic Algorithms
Pasting
PCA
PDA
penalty
Perceptron
Perceptron’s
performance
Performance Measures
Performance Metrics
PERSPECTIVES AND ISSUES IN MACHINE LEARNING
Plotting customers with their segments
Polynomial Kernel
Pooling
Pooling Layer
Poor-
Poor-Quality Data
Positive Training Example
Precision
Precision and Recall
Precision/recall trade-off
Prediction
Preprocessing
Print
Process
Product Metrics
Product Strategy & Business Acumen
Programs
Projection
pull
push
Python
Q-Learning
Q–learning
Quadratic Programming
Random Forest
Random Forests
RandomForestClassifier
RBF
Read
Reader
Recall
Recommendation system
Regular Expression
Regularization
Regularization Hyperparameters
Regularization Parameter
reinforcement
Reinforcement Learning
Relationship to Dynamic Programming
ReLU
Remarks on Lazy and Eager Learning
repository
RNN
RNNLayer
RNNNode
ROC
rotation
Row
Scale
scaling
semi supervised
semi supervised learning
Sequential Covering Algorithms
SGD Classifier
SGDClassifier Model
Short Answer Questions
Sigmoid
simple learning process
sklearn
soft margin
Soft Margin Classification
Soft Voting
SoftMax
spam
spam classification
spam filter
Sparse Autoencoders
specific hypothesis
specific training examples.
stacking
Stride
Strings
supervised
supervised learning
Supervised/Unsupervised Learning
Support Vector Machine
Support Vector Machines
SVC
SVM
SVM algorithm
SVM Precision
SVM Regression
Syllabus
T-test
Tanh
target function
target concept
task
Temporal Difference Learning
TensorFlow
Testing
the Back-Propagation Algorithm
The Basic Decision Tree Learning Algorithm
The CART Training Algorithm
The Curse of Dimensionality
The Dual problem
The learning task
The Machine Learning Landscape
The Softmax Function
The Vanishing and Exploding Gradient Problems
Tolerance
traditional programming
Trainer and Optimizer
training
Training a Binary Classifier
Training and Visualizing a Decision Tree
training examples
Training Objective
Transfer Learning
True Negative Rate
True Positive Rate
Turing Machines
Types
Types of Machine Learning Systems
Under Fitting
Under the Hood
Underfitting
Unsupervised
Unsupervised and Reinforcement Learning
unsupervised learning
upstream
Vanilla RNNs
variational autoencoders
Version Spaces and the Candidate Elimination Algorithm
VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM
voting
Voting Classifiers
Weight Impact
Weight Initialization
WELL-POSED LEARNING PROBLEMS
What Is Machine Learning?
What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems Supervised/Unsupervised Learning Batch and Online Learning Instance-Based Versus Model-Based Learning
White Box
Why NN
why use machine learning
Why Use Machine Learning?
XML
Z - Test Contd.

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