This book aims to be an accessible introduction into the design and analysis of efficient algorithms. Throughout the book we will introduce only the most basic techniques and describe the rigorous mathematical methods needed to analyze them.

The topics covered include:

The divide and conquer technique.
The use of randomization in algorithms.
The general, but typically inefficient, backtracking technique.
Dynamic programming as an efficient optimization for some backtracking algorithms.
Greedy algorithms as an optimization of other kinds of backtracking algorithms.
Hill-climbing techniques, including network flow.

The goal of the book is to show you how you can methodically apply different techniques to your own algorithms to make them more efficient. While this book mostly highlights general techniques, some well-known algorithms are also looked at in depth. This book is written so it can be read from "cover to cover" in the length of a semester, where sections marked with a * may be skipped.

This textbook is an introductory coverage of algorithms and data structures with application to graphics and geometry.

This textbook is an introductory coverage of algorithms and data structures with application to graphics and geometry.

Table of Contents
Part I: Programming environments for motion, graphics, and geometry

1. Reducing a task to given primitives: programming motion
2. Graphics primitives and environments
3. Algorithm animation
Part II: Programming concepts: beyond notation

4. Algorithms and programs as literature: substance and form
5. Divide-and-conquer and recursion.
6. Syntax
7. Syntax analysis
Part III: Objects, algorithms, programs.

8. Truth values, the data type 'set', and bit acrobatics
9. Ordered sets
10. Strings
11. Matrices and graphs: transitive closure
12. Integers
13. Reals
14. Straight lines and circles
Part IV: Complexity of problems and algorithms

15. Computability and complexity
16. The mathematics of algorithm analysis
17. Sorting and its complexity
Part V: Data structures

18. What is a data structure?
19. Abstract data types
20. Implicit data structures
21. List structures
22. Address computation
23. Metric data structures
Part VI: Interaction between algorithms and data structures: case studies in geometric computation

24. Sample problems and algorithms
25. Plane-sweep: a general-purpose algorithm for two-dimensional problems illustrated using line segment intersection
26. The closest pair

The rationale of teaching analysis is to set the minimum content of Pure Mathematics required at undergraduate level for student of mathematics. It is important to note that skill in proving mathematical statements is one aspect that learners of Mathematics should acquire. The ability to give a complete and clear proof of a theorem is essential for the learner so that he or she can finally get to full details and rigor of analyzing mathematical concepts. Indeed it is in Analysis that the learner is given the exposition of subject matter as well as the techniques of proof equally. We also note here that if a course like calculus with its wide applications in Mathematical sciences is an end in itself then Analysis is the means by which we get to that end.

The rationale of teaching analysis is to set the minimum content of Pure Mathematics required at undergraduate level for student of mathematics. It is important to note that skill in proving mathematical statements is one aspect that learners of Mathematics should acquire. The ability to give a complete and clear proof of a theorem is essential for the learner so that he or she can finally get to full details and rigor of analyzing mathematical concepts. Indeed it is in Analysis that the learner is given the exposition of subject matter as well as the techniques of proof equally. We also note here that if a course like calculus with its wide applications in Mathematical sciences is an end in itself then Analysis is the means by which we get to that end.

Analysis I covers fundamentals of mathematical analysis: metric spaces, convergence of sequences and series, continuity, differentiability, Riemann integral, sequences and series of functions, uniformity, interchange of limit operations.

Continues 18.100, in the direction of manifolds and global analysis. Differentiable maps, inverse and implicit function theorems, n-dimensional Riemann integral, change of variables in multiple integrals, manifolds, differential forms, n-dimensional version of Stokes' theorem. 18.901 helpful but not required.

This collection of worksheets, homework assignments, and study skills exercises was created through a Round 14 Textbook Transformation Grant. The worksheets supplement the following topics as covered in OpenStax Precalculus: Functions, domain and range, rates of change, inverse functions, exponential functions, logarithmic functions, exponential growth modeling, angles, sine and cosine, right triangles, sum identities, and difference identities. Study skills exercises include growth mindset and metacognition activities.

This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.

Table of Contents
1 Why Statistics?
2 Describing Data
3 Correlation
4 Probability, Sampling, and Estimation
5 Foundations for inference
6 t-Tests
7 ANOVA
8 Repeated Measures ANOVA
9 Factorial ANOVA
10 More On Factorial Designs
11 Simulating Data
12 Thinking about answering questions with data
13 GIFs

Applied Calculus instructs students in the differential and integral calculus of elementary functions with an emphasis on applications to business, social and life science. Different from a traditional calculus course for engineering, science and math majors, this course does not use trigonometry, nor does it focus on mathematical proofs as an instructional method.

Welcome to applied calculus for computing. Calculation intended to be a general method of solving quantifiable problems. In the application of the calculation method, or as it is known the”infinitesimal” method, a problem is”divided into infinitesimal parts”(differentiation), analysed in its relations with the neighbouring parts and then”added”(integration) until the solution method. The two parts of this the analysis and synthesis form a model for more sophisticated methods based on calculation, used in applied science concepts you learn in calculus allow statistical , physicists and engineers create mathematical models of real situations and real problems and simulate their resolutions under different operating conditions.

Applied Combinatorics is an open-source textbook for a course covering the fundamental enumeration techniques (permutations, combinations, subsets, pigeon hole principle), recursion and mathematical induction, more advanced enumeration techniques (inclusion-exclusion, generating functions, recurrence relations, Polyá theory), discrete structures (graphs, digraphs, posets, interval orders), and discrete optimization (minimum weight spanning trees, shortest paths, network flows). There are also chapters introducing discrete probability, Ramsey theory, combinatorial applications of network flows, and a few other nuggets of discrete mathematics.
Applied Combinatorics began its life as a set of course notes we developed when Mitch was a TA for a larger than usual section of Tom’s MATH 3012: Applied Combinatorics course at Georgia Tech in Spring Semester 2006. Since then, the material has been greatly expanded and exercises have been added. The text has been in use for most MATH 3012 sections at Georgia Tech for several years now. Since the text has been available online for free, it has also been adopted at a number of other institutions for a wide variety of courses. In August 2016, we made the first release of Applied Combinatorics in HTML format, thanks to a conversion of the book’s source from LaTeX to MathBook XML. An inexpensive print-on-demand version is also available for purchase. Find out all about ways to get the book.

Since Fall 2016, Applied Combinatorics has been on the list of approved open textbooks from the American Institute of Mathematics.

Applied Combinatorics is an open-source textbook for a course covering the fundamental enumeration techniques (permutations, combinations, subsets, pigeon hole principle), recursion and mathematical induction, more advanced enumeration techniques (inclusion-exclusion, generating functions, recurrence relations, Polyá theory), discrete structures (graphs, digraphs, posets, interval orders), and discrete optimization (minimum weight spanning trees, shortest paths, network flows). There are also chapters introducing discrete probability, Ramsey theory, combinatorial applications of network flows, and a few other nuggets of discrete mathematics.
Applied Combinatorics began its life as a set of course notes we developed when Mitch was a TA for a larger than usual section of Tom’s MATH 3012: Applied Combinatorics course at Georgia Tech in Spring Semester 2006. Since then, the material has been greatly expanded and exercises have been added. The text has been in use for most MATH 3012 sections at Georgia Tech for several years now. Since the text has been available online for free, it has also been adopted at a number of other institutions for a wide variety of courses.

Since Fall 2016, Applied Combinatorics has been on the list of approved open textbooks from the American Institute of Mathematics.

Access also available here: http://www.rellek.net/appcomb/

Table of Contents
1. An Introduction to Combinatorics
2. Strings, Sets and Binomial Coefficients
3. Induction
4. Combinatorial Basics
5. Graph Theory
6. Partially Ordered Sets
7. Inclusion-Exclusion
8. Generating Functions
9. Recurrence Equations
10. Probability
11. Applying Probability to Combinatorics
12. Graph Algorithms
13. Network Flows
14. Combinatorial Applications of Network Flows
15. Polya's Enumeration Theorem
16. The Many Faces of Combinatorics
A. Epilogue
B. Background Material for Combinatorics
C. List of Notation

The wide range of examples in the text are meant to augment the "favorite examples" that most instructors have for teaching the topcs in discrete mathematics.

To provide diagnostic help and encouragement, we have included solutions and/or hints to the odd-numbered exercises. These solutions include detailed answers whenever warranted and complete proofs, not just terse outlines of proofs.

Our use of standard terminology and notation makes Applied Discrete Structures a valuable reference book for future courses. Although many advanced books have a short review of elementary topics, they cannot be complete.

The text is divided into lecture-length sections, facilitating the organization of an instructor's presentation.Topics are presented in such a way that students' understanding can be monitored through thought-provoking exercises. The exercises require an understanding of the topics and how they are interrelated, not just a familiarity with the key words.

An Instructor's Guide is available to any instructor who uses the text.

The wide range of examples in the text are meant to augment the "favorite examples" that most instructors have for teaching the topcs in discrete mathematics.

To provide diagnostic help and encouragement, we have included solutions and/or hints to the odd-numbered exercises. These solutions include detailed answers whenever warranted and complete proofs, not just terse outlines of proofs.

Our use of standard terminology and notation makes Applied Discrete Structures a valuable reference book for future courses. Although many advanced books have a short review of elementary topics, they cannot be complete.

The text is divided into lecture-length sections, facilitating the organization of an instructor's presentation.Topics are presented in such a way that students' understanding can be monitored through thought-provoking exercises. The exercises require an understanding of the topics and how they are interrelated, not just a familiarity with the key words.

An Instructor's Guide is available to any instructor who uses the text.

Table of Contents
1 Set Theory
2 Combinatorics
3 Logic
4 More on Sets
5 Introduction to Matrix Algebra
6 Relations and Graphs
7 Functions
8 Recursion and Recurrence Relations
9 Graph Theory
10 Trees
11 Algebraic Systems
12 More Matrix Algebra
13 Boolean Algebra
14 Monoids and Automata
15 Group Theory and Applications
16 An Introduction to Rings and Fields

The text covered a comprehensive extent of topics; ranging from introductory topics of linear equations to more advanced topics such as Game Theory.

This is a review of Applied Finite Mathematics by De Anza College Cupertino (https://louis.oercommons.org/courses/applied-finite-mathematics) completed by Jared Eusea, Assistant Professor of Mathematics at RPCC.

Laszlo Tisza was Professor of Physics Emeritus at MIT, where he began teaching in 1941. This online publication is a reproduction the original lecture notes for the course "Applied Geometric Algebra" taught by Professor Tisza in the Spring of 1976. Over the last 100 years, the mathematical tools employed by physicists have expanded considerably, from differential calculus, vector algebra and geometry, to advanced linear algebra, tensors, Hilbert space, spinors, Group theory and many others. These sophisticated tools provide powerful machinery for describing the physical world, however, their physical interpretation is often not intuitive. These course notes represent Prof. Tisza's attempt at bringing conceptual clarity and unity to the application and interpretation of these advanced mathematical tools. In particular, there is an emphasis on the unifying role that Group theory plays in classical, relativistic, and quantum physics. Prof. Tisza revisits many elementary problems with an advanced treatment in order to help develop the geometrical intuition for the algebraic machinery that may carry over to more advanced problems. The lecture notes came to MIT OpenCourseWare by way of Samuel Gasster, '77 (Course 18), who had taken the course and kept a copy of the lecture notes for his own reference. He dedicated dozens of hours of his own time to convert the typewritten notes into LaTeX files and then publication-ready PDFs. You can read about his motivation for wanting to see these notes published in his Preface below. Professor Tisza kindly gave his permission to make these notes available on MIT OpenCourseWare.

This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.

In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks.

Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals).

Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.

Table of Contents
1 Preface
2 Probability Systems
3 Minterm Analysis
4 Conditional Probability
5 Independence of Events
6 Conditional Independence
7 Random Variables and Probabilities
8 Distribution and Density Functions
9 Random Vectors and joint Distributions
10 Independent Classes of Random Variables
11 Functions of Random Variables
12 Mathematical Expectation
13 Variance, Covariance, Linear Regression
14 Transform Methods
15 Conditional Expectation, Regression
16 Random Selection
17 Conditional Independence, Given a Random Vector
18 Appendices

I designed the course for graduate students who use statistics in their research, plan to use statistics, or need to interpret statistical analyses performed by others. The primary audience are graduate students in the environmental sciences, but the course should benefit just about anyone who is in graduate school in the natural sciences. The course is not designed for those who want a simple overview of statistics; we’ll learn by analyzing real data. This course or equivalent is required for UMB Biology and EEOS Ph.D. students. It is a recommended course for several of the intercampus graduate school of marine science program options.