Online courses directory (4179)

Numerical analysis is the study of the methods used to solve problems involving continuous variables.  It is a highly applied branch of mathematics and computer science, wherein abstract ideas and theories become the quantities describing things we can actually touch and see.  The real number line is an abstraction where many interesting and useful ideas live, but to actually realize these ideas, we are forced to employ approximations of the real numbers.  For example, consider marking a ruler at sqrt{2}.  We know that sqrt{2} approx 1.4142, but if we put the mark there, we know we are in error for there is an infinite sequence of nonzero digits following the 2.  Even more: a number doesn’t have any width, yet any mark we make would have a width, and in that width lives an infinite number of real numbers.  You may ask yourself: isn’t it sufficient to represent sqrt{2} with 1.414?  This is the kind of question that this course will explore.  We have been trying to answer such questions for over 2,0…

Differential equations are, in addition to a topic of study in mathematics, the main language in which the laws and phenomena of science are expressed.  In basic terms, a differential equation is an expression that describes how a system changes from one moment of time to another, or from one point in space to another.  When working with differential equations, the ultimate goal is to move from a microscopic view of relevant physics to a macroscopic view of the behavior of a system as a whole. Let’s look at a simple differential equation.  Based on previous math and physics courses, you know that a car that is constantly accelerating in the x-direction obeys the equation d2x/dt2 = a, where a is the applied acceleration.  This equation has two derivations with respect to time, so it is a second-order differential equation; because it has derivations with respect to only one variable (in this example, time), it is known as an  ordinary differential equation, or an ODE. Let’s say that we want to sol…

Partial differential equations (PDEs) describe the relationships among the derivatives of an unknown function with respect to different independent variables, such as time and position. For example, the heat equation can be used to describe the change in heat distribution along a metal rod over time. PDEs arise as part of the mathematical modeling of problems connected to different branches of science, such as physics, biology, and chemistry. In these fields, experiment and observation provide information about the connections between rates of change of an important quantity, such as heat, with respect to different variables. These connections must be exploited to find an explicit way of calculating the unknown quantity, given the values of the independent variables  that is, to derive certain laws of nature. While we do not know why partial differential equations provide what has been termed the “unreasonable effectiveness of mathematics in the natural sciences” (the title of a 1960 paper by physicist…

The study of “abstract algebra” grew out of an interest in knowing how attributes of sets of mathematical objects behave when one or more properties we associate with real numbers are restricted.  For example, we are familiar with the notion that real numbers are closed under multiplication and division (that is, if we add or multiply a real number, we get a real number).  But if we divide one integer by another integer, we may not get an integer as a resultmeaning that integers are not closed under division.  We also know that if we take any two integers and multiply them in either order, we get the same resulta principle known as the commutative principle of multiplication for integers.  By contrast, matrix multiplication is not generally commutative.  Students of abstract algebra are interested in these sorts of properties, as they want to determine which properties hold true for any set of mathematical objects under certain operations and which types of structures result when we perform certain o…

This course is a continuation of Abstract Algebra I: we will revisit structures like groups, rings, and fields as well as mappings like homomorphisms and isomorphisms.  We will also take a look at ring factorization, which will lead us to a discussion of the solutions of polynomials over abstracted structures instead of numbers sets.  We will end the section on rings with a discussion of general lattices, which have both set and logical properties, and a special type of lattice known as Boolean algebra, which plays an important role in probability.  We will also visit an important topic in mathematics that you have likely encountered already: vector spaces.  Vector spaces are central to the study of linear algebra, but because they are extended groups, group theory and geometric methods can be used to study them. Later in this course, we will take a look at more advanced topics and consider several useful theorems and counting methods.  We will end the course by studying Galois theoryone of the most im…

This course is designed to introduce you to the rigorous examination of the real number system and the foundations of calculus of functions of a single real variable. Analysis lies at the heart of the trinity of higher mathematics algebra, analysis, and topology because it is where the other two fields meet. In calculus, you learned to find limits, and you used these limits to give a rigorous justification for ideas of rate of change and areas under curves. Many of the results that you learned or derived were intuitive in many cases you could draw a picture of the situation and immediately “see” whether or not the result was true. This intuition, however, can sometimes be misleading. In the first place, your ability to find limits of real-valued functions on the real line was based on certain properties of the underlying field on which undergraduate calculus is founded: the real numbers. Things may have become slightly more complicated when you began to work in other spaces. For instance, you may r…

Real Analysis II is the sequel to Saylor’s Real Analysis I, and together these two courses constitute the foundations of real analysis in mathematics. In this course, you will build on key concepts presented in Real Analysis I, particularly the study of the real number system and real-valued functions defined on all or part (usually intervals) of the real number line. The main objective of MA241 [1] was to introduce you to the concept and theory of differential and integral calculus as well as the mathematical analysis techniques that allow us to understand and solve various problems at the heart of sciencenamely, questions in the fields of physics, economics, chemistry, biology, and engineering. In this course, you will build on these techniques with the goal of applying them to the solution of more complex mathematical problems. As long as a problem can be modeled as a functional relation between two quantities, each of which can be expressed as a set of real numbers, the techniques used for real-value…

This course is an introduction to complex analysis, or the theory of the analytic functions of a complex variable.  Put differently, complex analysis is the theory of the differentiation and integration of functions that depend on one complex variable.  Such functions, beautiful on their own, are immediately useful in Physics, Engineering, and Signal Processing.  Because of the algebraic properties of the complex numbers and the inherently geometric flavor of complex analysis, this course will feel quite different from Real Analysis, although many of the same concepts, such as open sets, metrics, and limits will reappear.  Simply put, you will be working with lines and sets and very specific functions on the complex planedrawing pictures of them and teasing out all of their idiosyncrasies.  You will again find yourself calculating line integrals, just as in multivariable calculus.  However, the techniques you learn in this course will help you get past many of the seeming dead-ends you ran up against in…

This course will introduce you to a number of statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. First, we will review basic knowledge and skills that you learned in MA121: Introduction to Statistics [1]. Units 2-5 will introduce you to new ways to design experiments and to test hypotheses, including multiple and nonlinear regression and nonparametric statistics. You will learn to apply these methods to building models to analyze complex, multivariate problems. You will also learn to write scripts to carry out these analyses in R, a powerful statistical programming language. The last unit is designed to give you a grand tour of several advanced topics in applied statistics. [1] http://www.saylor.org/courses/ma121/…

This course will introduce you to the fundamentals of probability theory and random processes. The theory of probability was originally developed in the 17th century by two great French mathematicians, Blaise Pascal and Pierre de Fermat, to understand gambling. Today, the theory of probability has found many applications in science and engineering. Engineers use data from manufacturing processes to sample characteristics of product quality in order to improve the products being produced. Pharmaceutical companies perform experiments to determine the effect of a drug on humans and use the results to make decisions about treatment of illnesses, while economists observe the state of the economy over periods of time and use the information to forecast the economic future. In this course, you will learn the basic terminology and concepts of probability theory, including random experiments, sample spaces, discrete distribution, probability density function, expected values, and conditional probability. You will al…

This course is designed to provide you with a simple and straightforward introduction to econometrics.  Econometrics is an application of statistical procedures to the testing of hypotheses about economic relationships and to the estimation of parameters.  Regression analysis is the primary procedure commonly used by researchers and managers whether their employments are within the goods or the resources market and/or within the agriculture, the manufacturing, the services, or the information sectors of an economy. Completion of this course in econometrics will help you progress from a student of economics to a practitioner of economics.  By completing this course, you will gain an overview of econometrics, develop your ability to think like an economist, hone your skills building and testing models of consumer and producer behavior, and synthesize the results you find through analyses of data pertaining to market-based economic systems.  In essence, professional economists conduct studies that combine…

This course will introduce students to the field of computer science and the fundamentals of computer programming.  It has been specifically designed for students with no prior programming experience, and does not require a background in Computer Science.  This course will touch upon a variety of fundamental topics within the field of Computer Science and will use Java, a high-level, portable, and well-constructed computer programming language developed by Sun Microsystems, to demonstrate those principles.  We will begin with an overview of the topics we will cover this semester and a brief history of software development.  We will then learn about Object-Oriented programming, the paradigm in which Java was constructed, before discussing Java, its fundamentals, relational operators, control statements, and Java I/0.  The course will conclude with an introduction to algorithmic design.  By the end of the course, you should have a strong understanding of the fundamentals of Computer Science and the Java p…

This course is a continuation of the first-semester course titled Introduction to Computer Science I (CS101 [1]).  It will introduce you to a number of more advanced Computer Science topics, laying a strong foundation for future academic study in the discipline.  We will begin with a comparison between Javathe programming language utilized last semesterand C++, another popular, industry-standard programming language.  We will then discuss the fundamental building blocks of Object-Oriented Programming, reviewing what we learned last semester and familiarizing ourselves with some more advanced programming concepts.  The remaining course units will be devoted to various advanced Computer Science topics, including the Standard Template Library, Exceptions, Recursion, Searching and Sorting, and Template Classes.  By the end of the class, you will have a solid understanding of Java and C++ programming, as well as a familiarity with the major issues that programmers routinely address in a professional setting.

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

This class is offered as CS7641 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section! Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

*This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.* Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning! Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data. **Series Information**: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. - [Machine Learning 1: Supervised Learning](https://www.udacity.com/course/ud675) - [Machine Learning 2: Unsupervised Learning](https://www.udacity.com/course/ud741) (this course) - [Machine Learning 3: Reinforcement Learning](https://www.udacity.com/course/ud820) If you are new to Machine Learning, we suggest you take these 3 courses in order. The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

Topics covered in a traditional college level introductory macroeconomics course. Circular Flow of Income and Expenditures. Parsing Gross Domestic Product. More on Final and Intermediate GDP Contributions. Investment and Consumption. Income and Expenditure Views of GDP. Components of GDP. Examples of Accounting for GDP. Real GDP and Nominal GDP. GDP Deflator. Example Calculating Real GDP with a Deflator. Introduction to Inflation. Actual CPI-U Basket of Goods. Inflation Data. Moderate Inflation in a Good Economy. Stagflation. Real and Nominal Return. Calculating Real Return in Last Year Dollars. Relation Between Nominal and Real Returns and Inflation. Deflation. Velocity of Money Rather than Quantity Driving Prices. Deflation Despite Increases in Money Supply. Deflationary Spiral. Hyperinflation. Unemployment Rate Primer. Phillips Curve. Interest as Rent for Money. Money Supply and Demand Impacting Interest Rates. The Business Cycle. Aggregate Demand. Shifts in Aggregate Demand. Long-Run Aggregate Supply. Short Run Aggregate Supply. Demand-Pull Inflation under Johnson. Real GDP driving Price. Cost Push Inflation. Monetary and Fiscal Policy. Tax Lever of Fiscal Policy. Breakdown of Gas Prices. Short-Run Oil Prices. Keynesian Economics. Risks of Keynesian Thinking. Overview of Fractional Reserve Banking. Weaknesses of Fractional Reserve Lending. Full Reserve Banking. Money Supply- M0 M1 and M2. Simple Fractional Reserve Accounting part 1. Simple Fractional Reserve Accounting (part 2). MPC and Multiplier. Mathy Version of MPC and Multiplier (optional). Consumption Function Basics. Generalized Linear Consumption Function. Consumption Function with Income Dependent Taxes. Keynesian Cross. Details on Shifting Aggregate Planned Expenditures. Keynesian Cross and the Multiplier. Investment and Real Interest Rates. Connecting the Keynesian Cross to the IS-Curve. Loanable Funds Interpretation of IS Curve. LM part of the IS-LM model. Government Spending and the IS-LM model. Balance of Payments- Current Account. Balance of Payments- Capital Account. Why Current and Capital Accounts Net Out. Accumulating Foreign Currency Reserves. Using Reserves to Stabilize Currency. Speculative Attack on a Currency. Financial Crisis in Thailand Caused by Speculative Attack. Math Mechanics of Thai Banking Crisis.

Topics covered in a traditional college level introductory macroeconomics course. Introduction to Economics. Circular Flow of Income and Expenditures. Parsing Gross Domestic Product. More on Final and Intermediate GDP Contributions. Investment and Consumption. Income and Expenditure Views of GDP. Components of GDP. Examples of Accounting for GDP. Real GDP and Nominal GDP. GDP Deflator. Example Calculating Real GDP with a Deflator. Introduction to Inflation. Actual CPI-U Basket of Goods. Inflation Data. Moderate Inflation in a Good Economy. Stagflation. Real and Nominal Return. Calculating Real Return in Last Year Dollars. Relation Between Nominal and Real Returns and Inflation. Deflation. Velocity of Money Rather than Quantity Driving Prices. Deflation Despite Increases in Money Supply. Deflationary Spiral. Hyperinflation. Unemployment Rate Primer. Phillips Curve. Interest as Rent for Money. Money Supply and Demand Impacting Interest Rates. The Business Cycle. Aggregate Demand. Shifts in Aggregate Demand. Long-Run Aggregate Supply. Short Run Aggregate Supply. Demand-Pull Inflation under Johnson. Real GDP driving Price. Cost Push Inflation. Monetary and Fiscal Policy. Tax Lever of Fiscal Policy. Breakdown of Gas Prices. Short-Run Oil Prices. Keynesian Economics. Risks of Keynesian Thinking. Overview of Fractional Reserve Banking. Weaknesses of Fractional Reserve Lending. Full Reserve Banking. Money Supply- M0 M1 and M2. Simple Fractional Reserve Accounting part 1. Simple Fractional Reserve Accounting (part 2). MPC and Multiplier. Mathy Version of MPC and Multiplier (optional). Consumption Function Basics. Generalized Linear Consumption Function. Consumption Function with Income Dependent Taxes. Keynesian Cross. Details on Shifting Aggregate Planned Expenditures. Keynesian Cross and the Multiplier. Investment and Real Interest Rates. Connecting the Keynesian Cross to the IS-Curve. Loanable Funds Interpretation of IS Curve. LM part of the IS-LM model. Government Spending and the IS-LM model. Balance of Payments- Current Account. Balance of Payments- Capital Account. Why Current and Capital Accounts Net Out. Accumulating Foreign Currency Reserves. Using Reserves to Stabilize Currency. Speculative Attack on a Currency. Financial Crisis in Thailand Caused by Speculative Attack. Math Mechanics of Thai Banking Crisis.

Aggregate demand and aggregate supply. Keynesian thinking. Demand-pull and cost-push inflation. Fiscal and monetary policy. Aggregate Demand. Shifts in Aggregate Demand. Long-Run Aggregate Supply. Short Run Aggregate Supply. Demand-Pull Inflation under Johnson. Real GDP driving Price. Cost Push Inflation. The Business Cycle. Monetary and Fiscal Policy. Tax Lever of Fiscal Policy. Keynesian Economics. Risks of Keynesian Thinking. Aggregate Demand. Shifts in Aggregate Demand. Long-Run Aggregate Supply. Short Run Aggregate Supply. Demand-Pull Inflation under Johnson. Real GDP driving Price. Cost Push Inflation. The Business Cycle. Monetary and Fiscal Policy. Tax Lever of Fiscal Policy. Keynesian Economics. Risks of Keynesian Thinking.