統計與機器學習概論一(Introduction of Statistics and Machine Learning (I)) is designed 
<br>for machine learning beginners where many examples (but not all) are
<br>made for students with biomedical background. The class lays mathematical 
<br>foundations for its sequel class 統計與機器學習概論二(Introduction of Statistics and
<br>Machine Learning (II)) that we recommend against students to take if (I) or 
<br>similar has never been taken. These relevant mathematical concept and details
<br>include but not limited to relation between statistics and probability, concept of 
<br>and inference from sampling, distributions, linear algebra, linear transformation,
<br>regression and correlation, Goodness-of-fit test, Discriminant analysis, Training 
<br>Methods, Maximum Likelihood and Bayesian Parameter Estimation
<br>and ODEs applied for biomedical research, Decision Trees, Estimators, Decisions 
<br>and Machine Learning Basics. Python workshop is provided along with the class 
<br>where the successful completion of two homework in the class requires programming 
<br>skills.
<br>
<br>Teachers: 楊立威、張筱涵、洪樂文、李祈均老師 (Profs LW Yang, HH Chang, YW Hong, CC Lee)
<br>
<br>Textbooks:
<br>1.     https://www.amazon.com/Analysis-Biological-Data-Michael-
<br>Whitlock/dp/0981519407 (@NTHU library)
<br>2.     Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic
<br>Acids (@NTHU library)
<br>3.       
<br>
<br>Weeks:
<br>1.   9/14. From the things you already knew (to some extent) – mean, SD,
<br>variance, how is statistics related to probability? Comparing two means (doing it
<br>with your MS Excel sheet). Why does statistics/probability constitute the basics
<br>of machine learning (examples on drug development and exercise presc<x>ription)?
<br>(LW Yang)
<br>
<br>9/21, 9/28 holidays – Self-study on math chapters 
<br>
<br>2.   10/5. Read the math formula – index, difference between probability
<br>(discrete function) and probability density (continuous function), learning
<br>summation/multiplication signs in the games to align two protein sequences (LW
<br>Yang).
<br>3.   10/12 Distributions Rules Probability addition/multiplication, dependency,
<br>conditional probability, marginal probability and Bayes' theorem (LW Yang)
<br>4.   10/19. definition of important names, sampling biases and rules, Inference
<br>from samples A (w1, HH Chang)
<br>5.   10/26 Inference from samples B (w4, HH Chang)
<br>6.   11/2 Goodness-of-fit test, contingency analysis and one sample inference
<br>(w6/7, HH Chang)
<br>7.   11/9 Comparing of two means (continued), (Relative) Entropy, Information
<br>Content, “Distance” between Distributions, Boltzmann Relation and Normalization –
<br>frequency vs proportion, normalization by controls, by ranking, by probability (LW
<br>Yang)
<br>8.   11/16 Correlation and Regression (HH Chang)
<br>9.   11/23 ODE & Chain Rule & Back-propagation of la<x>yers of Perceptrons (by YW 
<br>Liu)
<br>10.  11/30 Detection Theory (YW Hong)
<br>11.  12/7  Estimation Theory (YW Hong)
<br>12.  12/14 Moving Towards Data-Driven Techniques (YW Hong)
<br>13.  12/21 Introduction to Machine Learning (CC Lee)
<br>14.  12/17 Manners of Learning (Supervised, Unsupervised, Semisupervised)(CC Lee)
<br>15.  1/4  ML Experiment Setup (Training, Testing, Cross validation)(CC Lee)
<br>16.  1/11 Final Exam
<br>
<br>Grading:
<br>Two math exams (10% each), two statistics/probability homework (10% each), two
<br>computational homework (15% each – Protein Sequence Aligner & CpG island predictor
<br>using log-odds) due on 1/16, 2022.
<br>Final(30%)
<br> 
<br>With programming workshop:
<br>Python programming taught in 10+ weeks + one session of basic Linux