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2023-11-22 01:10:33
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8715
LeetCode
Linux
机器学习
数据清洗
Lec 1-6
Lec 7-14
12. Record linkage
13-14. Pre-proccessing & Blocking
Lec 15 - 16 Record pair comparison
Lec 17-18 Record pair classification
19. Record linkage evaluation
20. Clerical review
21. Data Fusion
22. Advanced record linkage techniques
23. Privacy aspects in data wrangling and privacy-preserving record linkage
24. Ontology matching 本体匹配
25. Wrangling dynamic and spatial data
文档分析｜自然语言处理
IR
Information retrieval (IR) L1
Information retrieval (IR) L2
Information retrieval (IR) L3
Web search
Mechine learning
L1 Linear Regression
L2 Representation in NLP
L3 Deep Neural Networks
L4 Deep Neural Networks in Practice
L5 Deep Neural Networks for Structured Data
L6 DNN – Attention
L7 DNN-Transformers
L8 Pre-training and Neural Language Models
L9 Clustering
NLP
L1 Semantics
L2 Constituency Parsing 成分句法分析
L3 Dependency Parsing 依赖解析
L4 Language Models
数据挖掘
问题汇总
quiz
分类和预测（Classification & Prediction）
Two steps: Construct and Evaluate
Evaluation
Decision Trees 决策树
Basic, greedy, decision tree algorithm
Attribute Selection Methods
Information Gain
Information Gain for continuous-valued attributes
Gain Ratio
Gini index
Other Attribute Selection Methods
Overfitting and tree pruning
Enhancements to the Basic Algorithm
Extracting Rules from Decision Trees
Rule learning 规则学习
Rule-Based Classification
Rule Induction
Bayes Classifiers 贝叶斯分类器
What is Bayesian classifier?
Basic Probabilities
Bayes' Theorem
Limitation
Naive Bayes 朴素贝叶斯
Laplacian Correction
Numerical attributes
Bayesian Belief Networks
Training a Belief Network
Evaluation of Classifiers
Model Evaluation and Selection
Evaluation metrics for classification
Estimating a classifier’s accuracy
ROC Curve
Comparing classifiers
Exercises
Other issues affecting the quality of a model
Lazy Learners
Case-Based Reasoning (CBR)
K-Nearest Neighbourhood (k-NN)
SVM(Support vector machine)支持向量机
Linearly Separable Case 线性可分
Linearly Inseparable Case 线性不可分情况
Neural Nets 神经网络
Multi-Layer Feed-Forward Neural Network
Prediction with Neural Network
Training a Neural Network
Performance Evaluation
Other Neural Net Architectures
Linear Regression 线性回归
Simple Linear Regression
Multiple Linear Regression
Variants of Classification
Other "Soft" Classification Algorithms
Cluster Analysis 聚类分析
Clustering: Basic Concepts
Quality of Clustering
Considerations
Major Approaches
Partitioning Methods (K-means)
Strength and Weakness
K-Medoids (PAM)
Exercise: K-means clustering
Hierarchical Clustering (AGNES and DIANA)
Distance between Clusters
Dendrogram 系统树图
Density-Based Clustering
Grid-Based Approach
Evaluation of Clustering
Assessing Clustering Tendency
Determine the Number of Clusters, k
Measure Clustering Quality
Outlier Detection
What are Outliers?
Statistical Approaches
Parametric Methods: Univariate Outliers from a Normal Distribution
Parametric Methods: Multivariate Outliers
Parametric Methods: Mixture of Parametric Distributions
Proximity-Based Approaches 基于邻近的方法
Distance-Based Outlier Detection: Nested loop method
Example: Distance-Based Outlier with Nested Loop
Density-based Outlier Detection
Clustering Based Approaches 基于聚类的方法
Classification-Based Approaches
Contextual and Collective Outliers
Specialist topics
Data Stream Mining 数据流挖掘
Stream OLAP
Synopsis Methods for Stream Data Processing 流数据处理的简要方法
Frequent Pattern Mining
Clustering
Ensemble Methods
Bagging
Boosting with AdaBoost
Example: AdaBoost ensemble method
Time Series Analysis 时间序列分析
Trend Analysis
Similarity Search 相似性搜索
Text and Web Mining
Basic Measures for Information Retrieval
Web mining
Mining Page Structure
Mining Web link structure
Mining Multimedia on the Web
Web Usage Mining
Word meaning by embedding
Text mining problems
Keyword based association analysis
Text Classification
Document Clustering
Text Data Analysis and Information Retrieval
Information Retrieval Techniques
Selecting index terms
building an index
Matching the query to the index
How to assign weights to term occurrences
Putting it together: Ranking in the vector space model
Semantic Web and Knowledge GraphsBook
Semantic Web Mining 语义网挖掘
OWL learning: Example
Refinement-based Classification
Summary
introduction
Foundation technologies
Universal Resource Identifiers
URI structure
RDF/RDFs
RDF for relations
How to encode Literals that are not things
Namespace abbreviations
A graph is a set of Triples
Typed Literals
Things can also be typed
SPARQL: Querying RDF
The Web Ontology Language 网络本体语言(OWL)
OWL preliminaries
OWL Classes
Relating Classes and Properties
OWL individuals
Knowledge Graphs
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