Lecture 1

• Course Introduction
• Data mining is the process of semi-automatically analyzing large databases to find useful patterns
  
• Grouping
• Reading: Machine learning for bioinformatics and neuroimaging (pdf)
• Online course: Data Mining in Bioinformatics (link)
• Types of Data Mining Tasks
  • Prediction based on past history (mechanism examples)
    • Classification
      • Items (with associated attributes) belong to one of several classes
      • Training instances have attribute values and classes provided
      • Given a new item whose class is unknown, predict to which class it belongs based on its attribute values
    • Regression
      • Given a set of mappings for an unknown function, predict the function result for a new parameter value
      • Regression deals with the prediction of a value, rather than a class.
  • Descriptive Patterns
    • Associations
      • Find books that are often bought by “similar” customers. If a new such customer buys one such book, suggest the others too.
      • Associations may be used as a first step in detecting causation. E.g., association between exposure to chemical X and cancer
      • An association rule must have an associated population; the population consists of a set of instances. Rules have an associated support, as well as an associated confidence.
    • Clusters
      • Detection of clusters remains important in detecting epidemics. E.g., typhoid cases were clustered in an area surrounding a contaminated well
      • Clustering: Intuitively, finding clusters of points in the given data such that similar points lie in the same cluster.
  • Classification Rules
    • Decision Tree Classifiers
      • Each internal node of the tree partitions the data into groups based on a partitioning attribute, and a partitioning condition for the node
      • 
    • Bayesian Classifiers, Naïve Bayesian Classifiers
      • naïve Bayesian classifiers assume attributes have independent distributions, and thereby estimate
    • Support Vector Machine Classifiers
      • SVMs can be used separators that are curve, not necessarily linear, by transforming points before classification
      • 
    • Neural Network Classifiers
      • Neural network has multiple layers. For classification, each output value indicates likelihood of the input instance belonging to that class
      • 
      • Deep neural networks have a large number of layers with large number of nodes in each layer
      • Deep learning refers to training of deep neural network on very large numbers of training instances
• Other Types of Mining
  • Text mining: application of data mining to textual documents
  • Sentiment analysis: E.g., learn to predict if a user review is positive or negative about a product
  • Information extraction: Create structured information from unstructured textual description or semi-structured data such as tabular displays
  • Entity recognition and disambiguation: E.g., given text with name “Michael Jordan” does the name refer to the famous basketball player or the famous ML expert
  • Knowledge graph: Can be constructed by information extraction from different sources, such as Wikipedia, Pubmed.
• Deep Learing深度学习
  • 基础和基本思想
    1. 1. 人工智能、计算智能、类脑智能
    2. 2. 机器学习、记忆学习、归纳学习、统计学习
    3. 3. 深度学习
    4. 4. 人工神经网络、前馈神经网络、BP 算法 、Hessian 矩阵、 结构性特征表示
  • 基本框架结构
    1. 1. Tensorflow
    2. 2. Caffe
    3. 3. Torch
    4. 4. MXNet
  • CNN卷积神经网络
    1. 1. CNN 卷积神经网络之卷积层（一维卷积、二维卷积） 、池化层（均值池化、最大池化） 、全连接层、激活函数层、Softmax 层
    2. 2. CNN 卷积神经网络改进 R-CNN（SPPNET）、Fast-R-CNN、Faster-R-CNN（YOLO、SSD）
    3. 3. 深度学习的模型训练
    4. 4. 梯度下降的优化方法
  • RNN循环神经网络
    1. 1. RNN 循环神经网络之梯度计算、BPTT
    2. 2. RNN 循环神经网络改进 LSTM、GRU、Bi-RNN、Attention based RNN
    3. 3. RNN 实际应用 Seq2Seq 的原理与实现
  • DRL强化学习
    1. 1. 强化学习的理论
    2. 2. 经典模型 DQN
    3. 3. AlphaGo 原理
    4. 4. RL 实际应用AlphaGo
       
  • GAN对抗性生成网络
    1. 1. GAN 的理论知识
    2. 2. GAN 经典模型 CGAN、LAPGAN、DCGAN
    3. 3. GAN 经典模型 INFOGAN、WGAN、S2-GAN
    4. 4. GAN 实际应用 DCGAN 提高模糊图片分辨率
    5. 5. GAN 实际应用 InfoGAN 做特定的样本生成
  • TL 迁移学习
    1. 1. 迁移学习的理论
    2. 2. 迁移学习的常见方法 特征、实例、数据、深度迁移、强化迁移、研究案例
  • 算法理论解析
    1. 1. 基于区域卷积网络 RCNN
    2. 2. 深度残差网络 Resnet
    3. 3. 胶囊网络 Capsule
    4. 4. 长短时记忆网络 LSTM
    5. 5. 注意力机制
    6. 6. BP 反传算法
    7. 7. 可变分编码器 VAE
  • 实际应用案例操作
    1. 1. CNN—>图像分类
    2. 2. Lstm—>文本分类
    3. 3. Lstm—>命名实体抽取
    4. 4. Yolo—>目标检测
    5. 5. 图像分类（CNN）
    6. 6. 目标定位和识别（RCNN）
    7. 7. 图像重建（Auto-encoder）
    8. 8. 文本识别（RNN）
    9. 9. 实体标注（LSTM）
    10. 10. 手写体数字生成（GAN）
    11. 11. 逻辑回归导出图像分类
    12. 12. 静/动态图编写（CNN）
  • 深度学习 DeepLearning 的常用 模型或者方法
    
    1. 1. AutoEncoder 自动编码器
    2. 2. Sparse Coding 稀疏编码
    3. 3. Restricted Boltzmann Machine (RBM) 限制波尔兹曼机
    4. 4. Deep Belief Networks (DBN) 深度置信网络

Lecture 2

Lecture 3

• Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.
• Classification（分类）Clustering（聚类）
• Statistical Learning（统计学习）
• Association Analysis（关联分析）
• Link Mining（链接挖掘）
• Bagging and Boosting（自举汇聚法和迭代算法，属于集成学习ensemble learning）
• Sequential Patterns（序列模式）
• Integrated Mining（集成挖掘）
• Rough Sets（粗糙集，属性约简算法）
• Graph Mining（图挖掘）

【十大经典数据挖掘算法】系列

• Classification: C4.5
• Clustering: K-Means
• Statistical Learning: SVM
• Association Analysis: Apriori
• Statistical Learning: EM
• Link Mining: PageRank
• Bagging and Boosting: AdaBoost
• Classification: kNN
• Classification: Naïve Bayes
• Classification: CART
• Feature selection / pattern recognition
• group reporting

Lecture 4

• Pattern evaluation
• group reporting

Lecture 5

• Statistics model
• group reporting

Lecture 6

• Knowledge presentation
• group reporting

Lecture 7

• Prelimary results & Discussions
• 10 Challenging Problems in Data Mining Research
  • Developing a Unifying Theory of Data Mining
  • Scaling Up for High Dimensional Data/High Speed Streams
  • Mining Sequence Data and Time Series Data
  • Mining Complex Knowledge from Complex Data
  • Data Mining in a Graph Structured Data
  • Distributed Data Mining and Mining Multi-agent Data
  • Data Mining for Biological and Environmental Problems
  • Data-Mining-Process Related Problems
  • Security, Privacy and Data Integrity
  • Dealing with Non-static, Unbalanced and Cost-sensitive Data

Lecture 8

• Seminar
• final report