The class has two main goals for students: (1) learn the general theory and algorithmic procedures of several widely used statistical models; (2) develop fluency in statistical programming skills. The class puts more emphasis on implementation instead of statistical theories, although a non-trivial amount of theories will be taught in the class. Students will gain computational skills and practical experiences on simulations and statistical modeling.
This course requires significant amount of programming. Each set of homework involves the analysis of real dataset or implementation of certain algorithms using high-level programming language (Matlab/R/perl/python).
Upon successfully completing this course, students will be able to:
The target audiences of the class include Biostatistics and Bioinformatics Ph.D. students, or other graduate students who works on highly quantitative fields.
Prerequisites: BIOS 510 or equivalent. BIOS 545 “Introduction to R programming” or prior programming experiences in one or more high-level programming languages (Matlab/R/perl/python).
Grading: Four sets of homework for a total of 90%. Class attendence and participation, 10%.
Date | Lecture Title | Description | Homework |
8/25 (Thurs) | Lecture 1: Random number generation, permutation and bootstrap (Wu) [Notes] | Course information. Random number generation. permutation and bootstrap. | |
8/30 (Tues) | Lecture 2: Optimization methods (Wu) [Notes] | Optimization Algorithms: Newton-Raphson, Quasi-Newton-Raphson, Iteratively Re-weighted Least Squares for Generalized Linear Regression | homework1 |
9/1 (Thurs) | Lecture 3: EM algorithm I (Wu) [Notes] | EM algorithm and applications. | |
9/6 (Tues) | Lecture 4: EM algorithm II [Notes], information theory [Notes] | EM algorithm extensions, SEM algorithm, EM gradient algorithm, ECM algorithm. Introduction to information theory. | |
9/8 (Thurs) | Lecture 5: MM algorithm (Wu) [Notes] | MM algorithm and applications | homework2 |
9/13 (Tues) | Lecture 6: HMM I (Wu) [Notes] | Introduction to HMM. Forward-backward algorithm. | |
9/15 (Thurs) | Lecture 7: HMM II (Wu) [Notes] | Viterbi algorithm. | |
9/20 (Tues) | Lecture 8: Linear programming I (Wu) [Notes] | Introduction. Simplex algorithm. Duality. | |
9/22 (Thurs) | Lecture 9: Linear programming II (Wu) [Notes] | Interior point method. Quadratic programming. Applications of LP: quantile regression. | |
9/27 (Tues) | Lecture 10: Linear programming III (Wu) [Notes] | Applications of LP: LASSO, Support vector machine. | homework3, Data for questions 1 |
9/29 (Thur) | Lecture 11: MCMC I (Qin) [Notes] | Introduction to MCMC. | |
10/4 (Tues) | Lecture 12: MCMC II (Qin) [Notes] | Introduction to sequential MC. | |
10/6 (Thur) | Lecture 13: MCMC III (Qin) [Notes, handout] | Other techniques, applications of MCMC. | homework4 |
10/11 (Tues) | Fall break, no class | ||
10/13 (Thur) | Lecture 14: Dimension reduction (Wu) [Notes] | Introduce two dimension reduction methods: non-negative matrix factorization (NMF), and local Dirichlet allocation (LDA). | |
10/18 (Thur) | Lecture 14: Introduction to deep learning [Notes] | Perceptron, neural network, deep architect, convolutional neural network, transfer learning. |