STAT3612 Data Mining (Statistical Machine Learning)

HKU 2018-19 Semester 2

Course Syllabus: PDF

Instructor: Dr. Aijun Zhang (ajzhang at hku dot hk; RR224)
Tutor: Dr. Gilbert Lui (csglui at hku dot hk; RR118)
Lecture Hours:
Monday 5:30pm — 6:20pm (T3)
Thursday 4:30pm — 6:20pm (T3)
Tutorial Hours:
Monday 4:30pm — 5:20pm (RR101)
Tuesday 9:30am — 10:20am (RR101)

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 Tutorial1A.ipynb
Lecture 3: Jan 28-Feb 14 Generalized Linear Models (Slides)
Linear Regression, Logistic Regression
Multinomial Logit, CoxPH model
Lecture3.ipynb Tutorial1B.ipynb
Lecture 4: Feb 18-21 Basis Expansion (Slides)
Feature Engineering,
Nonparametric Regression
Lecture4.ipynb Tutorial2A.ipynb
Lecture 5: Feb 25-28 Structural Regularization I (Slides)
Regularized generalized linear models
Ridge Regression, Lasso, Sparse Modeling
Homework 1: Due 12/3 Tutorial2B.ipynb
Lecture 6: March 11-14 Structural Regularization II (Slides)
Nonparametric regression
Smoothing spline, Piecewise smoothness
Lecture6Rnb.html
Project Proposal: Due 27/3
Lecture 7: March 18-21 Stochastic Optimization
First-order method, GD Demo
Large-scale logistic modeling
Lecture7.html
Test 1: 18/3 (Monday)
Past papers: a, b
Tutorial3.ipynb
Lecture 8: March 25-28 Tree-based Methods (HTML)
Classification and Regression Trees
Bagging, Random Forest, Boosting
Lecture8.ipynb
TalkCN.pdf
Tutorial4.ipynb
Lecture 9: April 1 Support Vector Machines (Slides)
Separating Hyperplane, Kernel method,
Hyperparameter optimization, AutoML
Lecture 10: April 4-11 Neural Networks (Slides)
MLP, Backpropagation algorithm, Deep Learning
Lecture10.ipynb

Demo.html

Tutorial5.ipynb
Lecture 11: April 15 Interpretable Machine Learning
Explainable Neural Networks, SOSxNN
TalkEN.pdf
SOSxNN.pdf
Homework 2: Due 4/5
Lecture 12: April 18 Unsupervised Learning (HTML)
K-means, PCA, t-SNE
Test 2: 18/4 (Thursday)
Past papers: a, b
Apr 23,25 Group Project Presentation Report Template