Who / When / Where
Instructor: Zhiyu Wan
Teaching Assistant: Feiyang Zhou
Semester: Fall 2026
Location & Time:
– SIST 1A106, 15:00-16:40 on Mondays
– SIST 1D108, 10:15-11:55 on Wednesdays (Even Week)
Office Hours: Upon request, Location: BME Building, Room 228
Schedule
(Note: This schedule is tentative and subject to change.)
| Week | Chapter | Teaching Contents | Readings | Assignments |
| Week 1, Monday Sept 14, 15:00-16:40 Lecture 1 (PDF) | I. Course Introduction (a) | Course introduction. Introduction to medical statistics. Concepts of statistics. | (Optional: 1.《Fundamentals of Biostatistics》Ch.1. 2.《医学统计学（高教版）》Ch.1. 3.《医学统计学（人卫版）》Ch.1.) | / |
| Week 2, Monday Sept 21, 15:00-16:40 Lecture 2 (PDF) | I. Course Introduction (b) | Descriptive statistics. | (Optional: 1.《Fundamentals of Biostatistics》Ch.2. 2.《医学统计学（人卫版）》Ch.2.) | / |
| Week 2, Wednesday Sept 23, 10:15-11:50 Lecture 3 (PDF) | II. Data Visualization (a) | In-class quiz. Introduction to R. | ||
| Week 3, Monday Sept 28, 15:00-16:40 Lecture 4 (PDF) Practice | II. Data Visualization (b) | Introduction to Python. | ||
| Week 4, Saturday Oct 10, 10:15-11:50 Lecture 5 (PDF) | III. Probability and Probability Distributions (a) | Bayesian inference. Receiver operating characteristic (ROC) curves. | (Optional: 1.《Fundamentals of Biostatistics》Ch.3.) | |
| Week 5, Monday Oct 12, 15:00-16:40 Lecture 6 (PDF) | III. Probability and Probability Distributions (b) | Discrete probability distributions. Continuous probability distributions. | (Optional: 1.《Fundamentals of Biostatistics》Chs.4&5. 2.《医学统计学（人卫版）》Ch.6. 3.《医学统计学（高教版）》Ch.2.) | |
| Week 6, Monday Oct 19, 15:00-16:40 Lecture 7 (PDF) | IV. Estimation and Hypothesis Testing (a) | Randomized experimental design and sampling. Point estimations. Confidence intervals. | (Optional: 1.《Fundamentals of Biostatistics》Ch.6.) | |
| Week 6, Wednesday Oct 21, 10:15-11:50 Lecture 8 (PDF) | IV. Estimation and Hypothesis Testing (b) | One-Sample Inference. Statistical power and sample size estimation. | (Optional: 1.《Fundamentals of Biostatistics》Ch.7) | HW #1 Posted. |
| Week 7, Monday Oct 26, 15:00-16:40 Lecture 9 (PDF) | IV. Estimation and Hypothesis Testing (c) | Two-sample inference. Statistical experimental design approaches. | (Optional: 1.《Fundamentals of Biostatistics》Ch.8) | |
| Week 8, Monday Nov 2, 15:00-16:40 Lecture 10 (PDF) | V. Nonparametric Tests | Rank-sum tests. Chi-square tests. | HW #2 Posted. | |
| Week 8, Wednesday Nov 4, 10:15-11:50 Lecture 11 (PDF) | VI. Analysis of Variance | One-way analysis of variance. Two-way analysis of variance. Multiple comparisons in analysis of variance. 2 × 2 factorial design. Tests for homogeneity of variance. | HW #1 Due. | |
| Week 9, Monday Nov 9, 15:00-16:40 Lecture 12 (PDF) | Case Studies in Biomedical Engineering (including Guest Lecture) | Real-world studies. Healthcare big data. Data security and privacy protection. Guest lecture. | HW #2 Due. HW #3 Posted. | |
| Week 10, Monday Nov 16, 15:00-16:40 Lecture 13 (PDF) | VII. Data Classification (a) | K-nearest neighbors (KNN) algorithm. Support vector machines. Announce course project topics. | ||
| Week 10, Wednesday Nov 18, 10:15-11:50 Lecture 14 (PDF) | VII. Data Classification (b) | Naive bayes. Decision trees and random forests. Complete team formation. | HW #3 Due. HW #4 Posted. | |
| Week 11, Monday Nov 23, 15:00-16:40 Lecture 15 (PDF) | VIII. Regression Analysis (a) | Linear regression. Regularized regression. | ||
| Week 12, Monday Nov 30, 15:00-16:40 Lecture 16 (PDF) | VIII. Regression Analysis (b) | Polynomial regression. Logistic regression. | HW #4 Due. HW #5 Posted. | |
| Week 12, Wednesday Dec 2, 10:15-11:50 Lecture 17 (PDF) | IX. Supervised Learning (a) | Introduction to neural networks. Convolutional neural networks. Stochastic gradient descent. Complete project topic selection. | ||
| Week 13, Monday Dec 7, 15:00-16:40 Lecture 18 (PDF) | IX. Supervised Learning (b) | Recurrent neural networks. Transformers. Generative models. | ||
| Week 14, Monday Dec 14, 15:00-16:40 Lecture 19 (PDF) | X. Unsupervised Learning (a) | Hierarchical Clustering. Gaussian Mixture Models. Submission of team project proposals. | Project Proposal Report Due at 23:59 on Dec 14. | |
| Week 14, Wednesday Dec 16, 10:15-11:50 Lecture 20 (PDF) | X. Unsupervised Learning (b) | K-means clustering. Self-supervised learning models. | ||
| Week 15, Monday Dec 21, 15:00-16:40 Lecture 21 (PDF) | XI. Dimensionality Reduction | Correlation analysis. Principal component analysis. | HW #5 Due at 15:00 on Dec 21. | |
| Week 16, Monday Dec 28, 15:00-16:40 Lecture 22 (PDF) | XII. Time-Series Analysis | Survival analysis. Hidden Markov models. Markov chain Monte Carlo. | Final Project Presentation Due at 23:59 on Dec 29. | |
| Week 16, Wednesday Dec 30, 10:15-11:50 | Course Project Presentation | Group presentations and feedbacks. | Final Project Report (Research Paper) Due at 23:59 on Jan 8. |
