STAT3612 Data Mining (Statistical Machine Learning)
HKU 2018-19 Semester 2
Course Syllabus: PDF
Instructor: Dr. Aijun Zhang (ajzhang at hku dot hk; RR224) Moodle@HKU: http://moodle.hku.hk/ |
Class Schedule | Lecture Notes | Supplementary | Tutorial Notes |
Lecture 1: Jan 14-17 | Introduction to DS, ML, AI (Slides) Big Data, Data Science Venn Diagram, Machine Learning, Artificial Intelligence |
Lecture1.ipynb | |
Lecture 2: Jan 21-24 |
Data Exploration (Slides) Exploratory Data Analysis Data Visualization, Data Manipulation |
Lecture2.ipynb | Tutorial1.ipynb |
Lecture 3: Jan 28-31 |
Linear Regression LM, LSE, Model Inference Diagnostics, Variable Selection |
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Lecture 4: Feb 14 |
Basis Expansion Feature Engineering, Nonparametric Regression, GAM |
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Lecture 5: Feb 18-21 |
Regularization Smoothing spline, Piecewise smoothness, Ridge Regression, Lasso, Sparse Modeling |
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Lecture 6: Feb 25-28 |
Classification Logistic/Softmax regression LDA/QDA, kNN, Confusion matrix, ROC curve |
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Lecture 7: March 11-14 |
Stochastic Optimization First-order method, Large-scale logistic modeling |
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Lecture 8: March 18-21 |
Tree-based Methods Classification and Regression Trees Bagging, Random Forest, GBM |
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Lecture 9: March 25-28 |
Support Vector Machines Separating Hyperplane, Kernel method, Hyperparameter optimization, AutoML |
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Lecture 10: April 1-4 |
Neural Networks MLP, Backpropagation algorithm, Deep Learning |
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Lecture 11: April 8-11 |
Interpretable Machine Learning Explainable Neural Networks, SOSxNN |
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Lecture 12: April 15-18 |
Unsupervised Learning K-means, PCA, t-SNE |
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Apr 25 | Group Project Presentation |