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Video
Stanford Electrical Engineering Course on Convex Optimization.
 Course related:
 AMA4850 Optimization Methods
 Subjects:
 Mathematics and Statistics
 Keywords:
 Mathematical optimization Convex functions
 Resource Type:
 Video

Others
Linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and physical sciences.
 Course related:
 AAE3009 Operations Research and Computational Analytics in Air Transport Operations
 Subjects:
 Mathematics and Statistics
 Keywords:
 Linear programming
 Resource Type:
 Others

Courseware
This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include:
A complete set of Lecture Videos by Professor Gilbert Strang.
Summary Notes for all videos along with suggested readings in Prof. Strang’s textbook Linear Algebra.
Problem Solving Videos on every topic taught by an experienced MIT Recitation Instructor.
Problem Sets to do on your own with Solutions to check your answers against when you’re done.
A selection of Java® Demonstrations to illustrate key concepts.
A full set of Exams with Solutions, including review material to help you prepare.
 Course related:
 AMA1120 Basic Mathematics II
 Subjects:
 Mathematics and Statistics
 Keywords:
 Algebras Linear
 Resource Type:
 Courseware

Video
With calculus well behind us, it's time to enter the next major topic in any study of mathematics. Linear Algebra! The name doesn't sound very intimidating, but there are some pretty abstract concepts in this subject. Let's start nice and easy simply by learning about what this subject covers and some basic terminology.
 Course related:
 COMP4434 Big Data Analytics
 Subjects:
 Mathematics and Statistics
 Keywords:
 Algebras Linear
 Resource Type:
 Video

Others
In these comprehensive video courses, created by Santiago Basulto, you will learn the whole process of data analysis. You'll be reading data from multiple sources (CSV, SQL, Excel), process that data using NumPy and Pandas, and visualize it using Matplotlib and Seaborn, Additionally, we've included a thorough Jupyter Notebook course, and a quick Python reference to refresh your programming skills.
 Course related:
 AMA1600 Fundamentals of AI and Data Analytics and AMA1751 Linear Algebra
 Subjects:
 Mathematics and Statistics and Computing
 Keywords:
 Computer programming Computer science Python (Computer program language)
 Resource Type:
 Others

Others
We offer mathematics in an enjoyable and easytolearn manner, because we believe that mathematics is fun.
 Subjects:
 Mathematics and Statistics
 Keywords:
 Mathematics
 Resource Type:
 Others

Video
Lecture videos from Gilbert Strang's course on Linear Algebra at MIT.
 Course related:
 AMA1120 Basic Mathematics II  Calculus and Linear Algebra
 Subjects:
 Mathematics and Statistics
 Keywords:
 Algebras Linear
 Resource Type:
 Video

Video
This course is an introduction to game theory and strategic thinking. Ideas such as dominance, backward induction, Nash equilibrium, evolutionary stability, commitment, credibility, asymmetric information, adverse selection, and signaling are discussed and applied to games played in class and to examples drawn from economics, politics, the movies, and elsewhere.
 Subjects:
 Mathematics and Statistics and Economics
 Keywords:
 Game theory
 Resource Type:
 Video

MOOC
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute pvalues and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
 Subjects:
 Statistics and Research Methods and Mathematics and Statistics
 Keywords:
 Life sciences  Statistical methods Mathematical statistics  Data processing R (Computer program language)
 Resource Type:
 MOOC

Others
Euclid’s Elements was a collection of 13 books about geometry originally written circa 300 BC. Shortly after the advent of the printing press, many editions and translations have been created over the centuries. Byrne’s 1847 edition of the first six books stands out for its unique use of colorful illustrations to demonstrate proofs rather than using letters to label angles, edges, and shapes. His edition was one of the first books to be published with such detailed use of colors and combined with its detailed diagrams makes it an impressive feat of publishing for the times and it stands out even today as a work of art. This site is a reproduction of Byrne’s Euclid by Oliver Byrne from 1847 that pays tribute to the beautiful original design and includes enhancements such as interactive diagrams, cross references, and posters designed by Nicholas Rougeux.
 Subjects:
 Mathematics and Statistics
 Keywords:
 Euclid's Elements Elements (Euclid) Geometry
 Resource Type:
 Others