This web page contains a free electronic version of my self-published textbook Algorithms, along with other lecture notes I have written for various theoretical computer science classes at the University of Illinois, Urbana-Champaign
This free online textbook is a one semester course in basic analysis. These were my lecture notes for teaching Math 444 at the University of Illinois at Urbana-Champaign (UIUC) in fall 2009. The course is a first course in mathematical analysis aimed at students who do not necessarily wish to continue a graduate study in mathematics. A Sample Darboux sums prerequisite for the course is a basic proof course. The course does not cover topics such as metric spaces, which a more advanced course would. It should be possible to use these notes for a beginning of a more advanced course, but further material should be added.
This book will initiate you into an esoteric world. You will learn and apply the methods of thought that mathematicians use to verify theorems, explore mathematical truth and create new mathematical theories. This will prepare you for advanced mathematics courses, for you will be better able to understand proofs, write your own proofs and think critically and inquisitively about mathematics.
Materials created by Larry Shrewsbury when he piloted the open source textbook “OpenIntro Statistics” during Fall 2016 through Spring 2017. These are MS Word documents so you can edit them to suit you.
MTH 243: Emphasizes the basic concepts and techniques of probability, descriptive, and inferential statistics. Topics include describing the distribution of data graphically and numerically, standard scores, normal distribution, empirical rule, sampling distributions, confidence intervals, hypothesis testing of both one and two populations, and linear regression. Introduces appropriate technology to display and analyze data.
MTH 244: Presents an assortment of tools from inferential statistics with an emphasis on applications. Reviews the concepts of hypothesis testing and confidence intervals. Introduces probability distributions of test statistics for various inferential statistical problems. Includes Analysis of Categorical Data (Chi-Square Goodness of Fit Test), Analysis of Variance (ANOVA), Nonparametric Statistics, and a brief introduction to Multiple Linear Regression. Applies the concepts and procedures with appropriate software tools for data analysis.
This textbook is intended to support courses that bridge the divide between mathematics typically encountered in U.S. high school curricula and the practical problems that natural resource students might engage with in their disciplinary coursework and professional internships.
SageMath is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers.