Textbooks:

1. Michael C. Whitlock and Dolph Schluter. 2014. The Analysis of Biological

Data (Second Edition). Roberts and Company Publishers, Greenwood Village,

Colorado. (ISBN-10: 1936221489) https://www.amazon.com/Analysis-Biological-Data-

Michael-Whitlock/dp/0981519407 (@NTHU library)

2. Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic

Acids (@NTHU library)

3. Peter Dayan & L. F. Abbott. Theoretical Neuroscience: Computational and

Mathematical Modeling of Neural Systems. The MIT Press (Physical and electronic

copies available @ NTHU library)

Date Topic Instructor

9/12 From the things you already knew (to some extent) – mean, SD,

variance, how is statistics related to probability? Hypothesis testing. Why does

statistics/probability constitute the basics of machine learning (examples on drug

development etc)? LW Yang

9/19 Read the math formula – index, difference between probability

(discrete function) and probability density (continuous function), Comparing two

means (doing it with your MS Excel sheet), learning from playing dynamics

programming games to align two biological sequences LW Yang

9/26 Distributions Rules Probability addition/multiplication, dependency,

conditional probability, marginal probability and Bayes' theorem LW Yang

10/3 Python quiz (5%), math quiz (5%) TA/LWYang

10/10 Build your first probabilistic model - a classifier, (Relative) Entropy,

Information Content, “Distance” between Distributions, Boltzmann Relation LW

Yang

10/17 Normalization – frequency vs proportion, normalization by controls, by

ranking, by probability LW Yang

10/24 Data types, sampling biases and rules, inference from samples I HH Chang

10/31 Inference from samples II HH Chang

11/7 Analyzing categorical data: goodness-of-fit test, contingency analysis HH

Chang

11/14 Correlation and Regression I HH Chang

11/21 Correlation and Regression II HH Chang

11/28 Models of neurons and synapses CC Lo

12/05 Models of synaptic plasticity and memory CC Lo

12/12 Matrix and linear algebra CC Lo

12/19 Stability and dynamical system theory CC Lo

12/26 Learning and decision making in biological neural networks CC Lo

Grading:

Yang: two computational homework (15% each – Protein Sequence Aligner & CpG island

predictor using log-odd values), one math exam and one python exam (10%)

Chang: homework (30%)

Lo: homework (30%)

With programming workshop:

Python programming taught in 10 weeks + one session of basic Linux

AI policy: (2)有條件開放，請註明如何使用生成式AI於課程產出 Conditionally open; please

specify how generative AI will be used in course output