Course Outline (list of lectures/topics each session) Week Topics Lab Activity Preparation Assignment 1 R as a mathematical Quick arithmetic using vectors Chapter 1 AND HW DUE: scratchpad: Scalars, vectors, and matrices (e.g., sweeping) Bring a laptop to matrices class! 2 data structures (e.g., data Descriptive statistics (means, Chapter 2 frame); transforming variables; variances, boxplots, missing data histograms, scatter plots) 3 Functional Programming; loops Extending the plot function Chapter 2 HW DUE Prob 2.2 (ab;c;de) and conditional operations with a user-defined “wrapper” plot function. 4 Density Estimation (statistical Write your own “rough” concepts) density estimation routine using boxcar weights. 5 [moving from scripts to Chapter 3 HW DUE Prob 3.2abc; 3.3 (ab;cd) functions] 6 Scatterplot smoothing Program Running means & (statistical concepts) medians to smooth. 7 [moving from scripts to HW DUE: Prob 3.1 (you are to with “play” functions] various choices for the parameter to “bw” the density function and then write just a few sentences about whether you discover a second mode with varying bandwidths (degree of smoothnes), NOT A JOURNAL ARTICLE WRITEUP) 8 Comparing two groups Simple linear regression Chapter 4 (program via optim & minimize SSR rather than math); intro to Bayesian learning 9 [some Bayesian disc. Relevant PROJ 1 DUE: Prob 4.1 to optimization or ‘learning’; use of in functions] … 10 Comparing more than two Program linear fit that Chapter 5 HW DUE: 5.2 groups minimizes MAD not SSR.
Course Outline (list of lectures/topics each session) Week Topics Lab Activity Preparation Assignment 1 R as a mathematical scratchpad: Scalars, vectors, matrices Quick arithmetic using vectors and matrices (e.g., sweeping) Chapter 1 AND Bring a laptop to class! HW DUE: 2 data structures (e.g., data frame); transforming variables; missing data Descriptive statistics (means, variances, boxplots, histograms, scatter plots) Chapter 2 3 Functional Programming; loops and conditional operations Extending the plot function with a user-defined “wrapper” plot function. Chapter 2 HW DUE Prob 2.2 (ab;c;de) 4 Density Estimation (statistical concepts) Write your own “rough” density estimation routine using boxcar weights. 5 [moving from scripts to functions] Chapter 3 HW DUE Prob 3.2abc; 3.3 (ab;cd) 6 Scatterplot smoothing (statistical concepts) Program Running means & medians to smooth. 7 [moving from scripts to functions] HW DUE: Prob 3.1 (you are to “play” with various choices for the “bw” parameter to the density function and then write just a few sentences about whether you discover a second mode with varying bandwidths (degree of smoothnes), NOT A JOURNAL ARTICLE WRITEUP) 8 Comparing two groups Simple linear regression (program via optim & minimize SSR rather than math); intro to Bayesian learning Chapter 4 9 [some Bayesian disc. Relevant to optimization or ‘learning’; use of … in functions] PROJ 1 DUE: Prob 4.1 10 Comparing more than two groups Program linear fit that minimizes MAD not SSR. Chapter 5 HW DUE: 5.2
APSTA-2352-syllabus.docx