3-Page work
homework/week-5/discussion-1-page.txt
This week our topic shifts to the classification concepts in chapter four. Therefore, answer the following questions: What are the various types of classifiers? What is a rule-based classifier? What is the difference between nearest neighbor and naïve bayes classifiers? What is logistic regression? -Page-1 -format-apa -reference-textbook mandatory
homework/week-5/IT632_Chapter 4 PPT - Beeline.html
Bright Dark Blues Grays Night- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 1
- Classification: Alternative Techniques
- Lecture Notes for Chapter 4
- Introduction to Data Mining
- by
- Tan, Steinbach, Kumar
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 2
- Types of Classifiers
- Binary vs. Multiclass
- Deterministic vs. Probablistic
- Linear vs. Nonlinear
- Global vs. local
- Generative vs. Discriminative
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 3
- Rule Based Classifiers
- How it works?
- Properties of a Rule Set
- Direct Methods for Rule Extraction
- Learn-One rule function
- Instance Elimination
- Indirect Methods for Rule Extraction
- Characteristics of Rule-Based Classifiers
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 4
- Learn-One rule function
- Instance Elimination
- Nearest Neighbor Classifiers
- Algorithm
- Computes the distance or similarity between each test instance and all training examples.
- Characteristics – Review 4.3.2
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 5
- Computes the distance or similarity between each test instance and all training examples.
- ve Bayes Classifier
- Basics of Probability Theory
- Bayes Theorem
- Bayes theorem presents the statistical principle for answering questions like the previous one, where evidence from multiple sources has to be combined with prior beliefs to arrive at predictions. Bayes theorem can be briefly described as follows.
- Classification
- Class conditional
- Generative classification
- Prior probabilty
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 6
- Bayes Theorem
- Bayes theorem presents the statistical principle for answering questions like the previous one, where evidence from multiple sources has to be combined with prior beliefs to arrive at predictions. Bayes theorem can be briefly described as follows.
- Classification
- Class conditional
- Generative classification
- Prior probabilty
- Bayesian Network
- Graphical Representation
- Conditional Independence
- Joint Probability
- Use of Hidden Variables
- Inference and Learning
- Variable Elimination
- Sum-Product Algorithm for Trees
- Generalizations for Non-Tree Graphs
- Learning Model Parameters
- Characteristics of Bayesian Networks
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 7
- Conditional Independence
- Joint Probability
- Use of Hidden Variables
- Variable Elimination
- Sum-Product Algorithm for Trees
- Generalizations for Non-Tree Graphs
- Learning Model Parameters
- Logistic Regression
- Generalized Linear Model
- Learning Model Parameters
- Characteristics
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 8
- Artificial Neural Network (ANN)
- Perceptron
- Learning the Perceptron
- Multi-layer Neural Network
- Learning Model Parameters
- Characteristics of ANN
- Universal approximators
- Review 4.7.3
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 9
- Learning the Perceptron
- Learning Model Parameters
- Universal approximators
- Review 4.7.3
- Deep Learning
- Using Synergistic Loss Functions
- Saturation of outputs and Cross entropy loss function
- Using Responsive Activation Functions
- Vanishing gradient problem and ReLU
- Regularization
- Dropout
- Initialization of Model Parameters
- Supervised and unsupervised pretraining
- Use of autoencoders and hybrid pretraining
- Characteristics of Deep Learning
- Review 4.8.5
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 10
- Saturation of outputs and Cross entropy loss function
- Vanishing gradient problem and ReLU
- Dropout
- Supervised and unsupervised pretraining
- Use of autoencoders and hybrid pretraining
- Review 4.8.5
- Support Vector Machine (SVM)
- Margin of a Separating Hyperplane
- Rationale for maximum margin
- Linear SVM
- Learning model parameters
- Soft-margin SVM
- Regularizer of Hinge Loss
- Nonlinear SVM
- Attribute transformation
- Learning a non-linear SVM Model
- Characteristics of SVM
- Review Section 4.9.5
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 11
- Rationale for maximum margin
- Learning model parameters
- Regularizer of Hinge Loss
- Attribute transformation
- Learning a non-linear SVM Model
- Review Section 4.9.5
- Ensemble Methods
- Rationale for Ensemble Methods
- Methods for Constructing an Ensemble Classifier
- Bias- Variance Decomposition
- Bagging
- Boosting
- AdaBoost
- Random Forests
- Empirical Comparison among Ensemble methods
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 12
- AdaBoost
- Class Imbalance Problem
- Building Classifiers with Class Imbalance
- Oversampling and undersampling
- Assigning scores to test instances
- Evaluating Performance with Class Imbalance
- Finding an Optimal Score Threshold
- Aggregate Evaluation of Performance
- ROC Curve
- Precision-Recall Curve
- © Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 13
- Oversampling and undersampling
- Assigning scores to test instances
- ROC Curve
- Precision-Recall Curve
- Multiclass Problem
- multiclass problem is one where the data is divided into more than two categories.
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homework/week-5/Week 5 Discussion - Beeline.html
Bright Dark Blues Grays NightThis week our topic shifts to the classification concepts in chapter four. Therefore, answer the following questions:
- What are the various types of classifiers?
- What is a rule-based classifier?
- What is the difference between nearest neighbor and naïve bayes classifiers?
- What is logistic regression?
homework/week-5/Week 5 Overview and Objectives - Beeline.html
Bright Dark Blues Grays Night
This week we focus on the various types of classifiers and understanding the key components to logistic regression.
Objectives:
- Define the various types of classifiers.
- Understand the key components to logic regression.
- Compare and contrast nearest neighbor and naïve Bayes classifiers.
homework/week-6/Week 6 Learning Materials - Beeline.html
Bright Dark Blues Grays Night
Read:
- Hemmatian, H. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(3), 1495–1545.
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homework/week-6/Week 6 Overview and Objectives - Beeline.html
Bright Dark Blues Grays Night
In week six, we turn our focus to a real world example on opinion mining and gain a better understanding of NLP framework.
Objectives:
- Discuss a real-world example on opinion mining and how it is used in information retrieval.
- Explain the various components and techniques of opinion mining and the importance to transforming an organizations NLP framework.
homework/week-6/week-6-homework.txt
Review the article by Hemmatian (2019), on classification techniques. In essay format answer the following questions: What were the results of the study? Note what opinion mining is and how it’s used in information retrieval. Discuss the various concepts and techniques of opinion mining and the importance to transforming an organizations NLP framework. In an APA7 formatted essay answer all questions above. There should be headings to each of the questions above as well. Ensure there are at least two-peer reviewed sources to support your work. The paper should be at least two pages of content (this does not include the cover page or reference page). -Pages-2 -Format-APA -References-Mandatory text book