Courses tagged with "Evaluation" (733)

This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns.

A modern VLSI chip has a zillion parts -- logic, control, memory, interconnect, etc.  How do we design these complex chips?  Answer: CAD software tools.  Learn how to build these tools in this class.

In this course, you will learn the fundamental computer science principles that power today’s apps. You will also create your own Android app using Java and standard software development tools.

Ce cours introduit la programmation orientée objet en l'illustrant en langage Java. Il présuppose connues les bases de la programmation (variables, types, boucles, fonctions, ...). Il est conçu comme la suite du cours «Initiation à la programmation (en Java)».

This course is about building 'web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm. In the past, this course has been offered at the Indian Institute of Technology Delhi as well as the Indraprastha Institute of Information Technology Delhi.

In this class you will look behind the scenes of image and video processing, from the basic and classical tools to the most modern and advanced algorithms.

This course covers database design and the use of database management systems for applications.

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!

Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.

In this course, you will learn about software defined networking and how it is changing the way communications networks are managed, maintained, and secured.

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research.

Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

The course is an introduction to linear and discrete optimization - an important part of computational mathematics with a wide range of applications in many areas of everyday life.

Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.

For anyone who would like to apply their technical skills to creative work ranging from video games to art installations to interactive music, and also for artists who would like to use programming in their artistic practice.

Learn about traditional and mobile malware, the security threats they represent, state-of-the-art analysis and detection techniques, and the underground ecosystem that drives such a profitable but illegal business.

This course is about learning to program well: building programs that are elegant, well tested and easy to maintain. The course is designed for students with no programming experience at all. Nonetheless, former students who already knew how to program have said it made them better programmers.

In this course--the second in a trans-institution sequence of MOOCs on Mobile Cloud Computing with Android--we will learn how to apply patterns, pattern languages, and frameworks to alleviate the complexity of developing concurrent and networked services on mobile devices running Android that connect to popular cloud computing platforms.

Learn about General Game Playing (GGP) and develop GGP programs capable of competing against humans and other programs in GGP competitions .

Introduction to statistics. We start with the basics of reading and interpretting data and then build into descriptive and inferential statistics that are typically covered in an introductory course on the subject. Overview of Khan Academy statistics. Statistics intro: mean, median and mode. Constructing a box-and-whisker plot. Sample mean versus population mean.. Variance of a population. Sample variance. Review and intuition why we divide by n-1 for the unbiased sample variance. Simulation showing bias in sample variance. Simulation providing evidence that (n-1) gives us unbiased estimate. Statistics: Standard Deviation. Statistics: Alternate Variance Formulas. Introduction to Random Variables. Probability Density Functions. Binomial Distribution 1. Binomial Distribution 2. Binomial Distribution 3. Binomial Distribution 4. Expected Value: E(X). Expected Value of Binomial Distribution. Poisson Process 1. Poisson Process 2. Introduction to the Normal Distribution. Normal Distribution Excel Exercise. Law of Large Numbers. ck12.org Normal Distribution Problems: Qualitative sense of normal distributions. ck12.org Normal Distribution Problems: Empirical Rule. ck12.org Normal Distribution Problems: z-score. ck12.org Exercise: Standard Normal Distribution and the Empirical Rule. ck12.org: More Empirical Rule and Z-score practice. Central Limit Theorem. Sampling Distribution of the Sample Mean. Sampling Distribution of the Sample Mean 2. Standard Error of the Mean. Sampling Distribution Example Problem. Confidence Interval 1. Confidence Interval Example. Mean and Variance of Bernoulli Distribution Example. Bernoulli Distribution Mean and Variance Formulas. Margin of Error 1. Margin of Error 2. Small Sample Size Confidence Intervals. Hypothesis Testing and P-values. One-Tailed and Two-Tailed Tests. Z-statistics vs. T-statistics. Type 1 Errors. Small Sample Hypothesis Test. T-Statistic Confidence Interval. Large Sample Proportion Hypothesis Testing. Variance of Differences of Random Variables. Difference of Sample Means Distribution. Confidence Interval of Difference of Means. Clarification of Confidence Interval of Difference of Means. Hypothesis Test for Difference of Means. Comparing Population Proportions 1. Comparing Population Proportions 2. Hypothesis Test Comparing Population Proportions. Squared Error of Regression Line. Proof (Part 1) Minimizing Squared Error to Regression Line. Proof Part 2 Minimizing Squared Error to Line. Proof (Part 3) Minimizing Squared Error to Regression Line. Proof (Part 4) Minimizing Squared Error to Regression Line. Regression Line Example. Second Regression Example. R-Squared or Coefficient of Determination. Calculating R-Squared. Covariance and the Regression Line. Correlation and Causality. Chi-Square Distribution Introduction. Pearson's Chi Square Test (Goodness of Fit). Contingency Table Chi-Square Test. ANOVA 1 - Calculating SST (Total Sum of Squares). ANOVA 2 - Calculating SSW and SSB (Total Sum of Squares Within and Between).avi. ANOVA 3 -Hypothesis Test with F-Statistic. Another simulation giving evidence that (n-1) gives us an unbiased estimate of variance. Mean Median and Mode. Range and Mid-range. Reading Pictographs. Reading Bar Graphs. Reading Line Graphs. Reading Pie Graphs (Circle Graphs). Misleading Line Graphs. Stem-and-leaf Plots. Box-and-Whisker Plots. Reading Box-and-Whisker Plots. Statistics: The Average. Statistics: Variance of a Population. Statistics: Sample Variance. Deductive Reasoning 1. Deductive Reasoning 2. Deductive Reasoning 3. Inductive Reasoning 1. Inductive Reasoning 2. Inductive Reasoning 3. Inductive Patterns.