Teachers: 楊立威、張筱涵、羅中泉老師 (Profs LW Yang, HH Chang, CC Lo)
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<br>Textbooks:
<br>1.     Michael C. Whitlock and Dolph Schluter. 2014. The Analysis of Biological 
<br>Data (Second Edition). Roberts and Company Publishers, Greenwood Village, 
<br>Colorado. (ISBN-10: 1936221489) https://www.amazon.com/Analysis-Biological-Data-
<br>Michael-Whitlock/dp/0981519407 (@NTHU library)
<br>2.     Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic
<br>Acids (@NTHU library)
<br>3. Peter Dayan & L. F. Abbott. Theoretical Neuroscience: Computational and 
<br>Mathematical Modeling of Neural Systems. The MIT Press (Physical and electronic 
<br>copies available @ NTHU library)
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<br>Date	Topic	Instructor
<br>9/13	From the things you already knew (to some extent) – mean, SD,
<br>variance, how is statistics related to probability? Hypothesis testing. Why does 
<br>statistics/probability constitute the basics of machine learning (examples on drug 
<br>development etc)?	LW Yang
<br>9/20	Read the math formula – index, difference between probability
<br>(discrete function) and probability density (continuous function), Comparing two 
<br>means (doing it with your MS Excel sheet), learning from playing dynamics 
<br>programming games to align two biological sequences 	LW Yang
<br>9/27 	Distributions Rules Probability addition/multiplication, dependency, 
<br>conditional probability, marginal probability and Bayes' theorem	LW Yang
<br>10/4 	Python quiz (5%), math quiz (5%)	TA/LWYang
<br>10/11	Build your first probabilistic model - a classifier, (Relative) Entropy, 
<br>Information Content, “Distance” between Distributions, Boltzmann Relation 	LW 
<br>Yang
<br>10/18 	Normalization – frequency vs proportion, normalization by controls, by 
<br>ranking, by probability	LW Yang
<br>10/25	Data types, sampling biases and rules, inference from samples I	HH Chang
<br>11/1 	Inference from samples II	HH Chang
<br>11/8	Analyzing categorical data: goodness-of-fit test, contingency analysis	HH 
<br>Chang
<br>11/15 	Correlation and Regression I	HH Chang
<br>11/22	Correlation and Regression II	HH Chang
<br>11/29	Models of neurons and synapses	CC Lo
<br>12/06	Models of synaptic plasticity and memory	CC Lo
<br>12/13	Matrix and linear algebra 	CC Lo
<br>12/20	Stability and dynamical system theory	CC Lo
<br>12/27	Learning and decision making in biological neural networks	CC Lo
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<br>Grading:
<br>Yang: two computational homework (15% each – Protein Sequence Aligner & CpG island 
<br>predictor using log-odd values), one math exam and one python exam (10%)
<br>Chang: homework (30%)
<br>Lo: homework (30%) 
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<br>With programming workshop:
<br>Python programming taught in 10 weeks + one session of basic Linux
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