Category: Cs 7641 final notes

Cs 7641 final notes

The information provided on this site is protected by U. This information is provided exclusively for the personal and academic use of students, instructors and other university personnel.

Use of this information for any commercial purpose, or by any commercial entity, is expressly prohibited. This information may not, under any circumstances, be copied, modified, reused, or incorporated into any derivative works or compilations, without the prior written approval of Koofers, Inc. A covariance matrix captures the correlation between variables in a data set. PCA finds the orthonormal eigenvectors of the covariance matrix as the basis for the transformed feature space.

Eigenvectors can be thought of as the "natural basis" for a given multi-dimensional data set. Higher eigenvalues in the covariance matrix indicate lower correlation between the features in the data set. PCA projections seek uncorrelated variables. Every data set has principle components, but PCA works best if data are Gaussian-distributed. For high dimensional data the Central Limit theorem allows us to assume Gaussian distributions. Covariance Matrices The variance of a single variable x is given by:?

All rights reserved. CS - Machine Learning. Georgia Institute of Technology-Main Campus.Download B. It is 3 years under graduation course that provides you wide career opportunities in Accounts, Commerce, management fields.

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cs 7641 final notes

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Machine Learning - 1.6 - Closing Remarks

Bears honor 3 with shutout of River Rats. By Joe Shetrom, Sports. Some game results are very befitting. Sunday's game was no exception, as the Hershey Bears honored the late Frank S. With the win, Hershey65 points remains four points behind Wilkes-Barre who defeated Philadelphiabut closed the gap to six points on Norfolk, and to five points on Philly, who each lost on Sunday. Hershey also finished the season series by sweeping Albany47 points in all eight contests this season, and have defeated the Rats in nine straight, dating back to last season.

Need to Know: Souza would continue his offensive outpouring of late, netting the game's first scoreand eventual game-winnerat the mark of the 1st period.

Souza, who has pocketed four of his five goals in February, punched in a loose puck from the crease, left there after Eric Perrin weaved around the Albany net and fired a shot that was initially stopped by Rats goalie Ari Ahonen. With just under six minutes remaining in the final period, Darche would ring up a power play insurance marker to give Hershey a advantage. Jeff Ulmer would set up the Bears' man advantage score, throwing a left circle pass back to Brett Clark at the point.

Clark would then blast a shot towards the Rats net, which was redirected by Darche and trickled through the Albany netminder's pads. Ulmer would put an exclamation point on the victory, picking up his 16th goal of the seasona power play tallyjust under three minutes later at of the 3rd. Perrin would earn another assist in the game, writing a shot from the left circle that was stopped by Ahonen, only to be picked up by Ulmer, who cashed in from the right side.

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Game Notes: Budaj earned five of Hershey's eight victories against Albany this season, allowing a total of 8 goals. Ahonen suffered the loss for the Rats, finishing with 30 saves … although each team had several scoring opportunities, Dean McAmmond may have had Albany's best scoring chance during the 3rd period, zipping around the Bears net to attempt a wrap-around on an open goal.

Martin Wilde would preserve the shutout though, getting a stick in front of the goal mouth to block McAmmond's low-ice shot … Hershey has outshot their opponent in four straight games … Ulmer finished with 3 points; his second, three-point game of the season Honoring "Mr. AHL President Dave Andrews also took to the podium, but it was Emrick who spoke the longest, telling humorous stories regarding the Bears legend.Preparing in advance is a good idea, since from the beginning you will need to review learn a lot of information before you can start working on the first assignment.

Many people feel overwhelmed due to all this work, and end up submitting a weak assignment. Because of that, a recommended preparation would be:. Try to understand them. Take notes. If you find many concepts too high level and would like an introduction, watch other videos like Andrew Ng's a very popular choice. If you took those classes in undergrad, you should be fine. If not, a MOOC on those topics could help. But it's recommended to use a language that you are already proficient in.

If you don't do that you will dedicate waste time to learn the language, while you could be using that precious time running experiments. It's important that you find a way to automate the execution of experiments with different parameters the caret library in R, scikit-learn in python, etc. Unless you have already worked extensively on ML and want to use this class to do something fancy, it's better to keep things simple. Choose datasets from the UCI Repositoryit's better if you choose classification datasets.

It's not a requirement, but again, if you are a newbie it's better not to overcomplicate things gigantic datasets, dirty datasets, etc. There's no hard rule, that's why many people "waste" time in this step. Once you have your "candidate" datasets, apply what you learned in the step 2 above, and run a few supervised learning algos over them and "see what happens". Fall course schedule with the list of readings is available here.

The required textbook for the course is Machine Learning by Tom Mitchell, Machine Learning with R: Notes on Rby Brent Wagenseller knitr : Elegant, flexible and fast dynamic report generation with R caret : Set of functions that attempt to streamline the process for creating predictive models. Tom Mitchell has posted old hws and exam material for his past classes:.Cost and management Accountancy CMA provides you an in-depth Knowledge to manage business with the available resources.

Cost accountants have to collect and analyze the financial information from all the areas of a company. Cost Accountants are one of the most vital functionaries of entire financial world.

Reinforcement Learning

Cost Accounting is a management accounting. It is specialized area of expertise concern with analyzing the cost of products, manufactured or sold by the company. CMA provide services, in investment planning, profit planning, project management and overall managerial decision making process.

Be Safe and Stay at Home. Skip to content. CMA Foundation. CMA Inter Group 1. CMA Inter Group 2. CMA Final Group 3. CMA Final Group 4.

cs 7641 final notes

Recent Students Result. Our Students Got Good Marks. There are 3 stage in CMA Course. Foundation Intermediate Final. Student are eligible to seek admission to the foundation course after qualification class 10th, but the students will be eligible for CMA Foundation exam only after qualifying class 12th. After completion of Foundation level, students will be eligible to move on to Intermediate level. After completing any group of intermediate level, students have to undergo 3 years practical training.

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CS 7641: Machine Learning

Students can watch videos within validity period for any number of time. There is no need of internet at the time of playing videos. We will also send courier receipt for tracking of book.In this video I am going to define what is probably the most common type of machine learning problem, which is supervised learning.

A while back, a student collected data sets from the Institute of Portland Oregon. Here on the horizontal axis, the size of different houses in square feet, and on the vertical axis, the price of different houses in thousands of dollars. So how can the learning algorithm help you? There might be a better one.

But each of these would be a fine example of a learning algorithm. So this is an example of a supervised learning algorithm. That is, we gave it a data set of houses in which for every example in this data set, we told it what is the right price so what is the actual price that, that house sold for and the job of the algorithm was to just produce more of these right answers such as for this new house, you know, that your friend may be trying to sell.

Namely the price. So technically I guess prices can be rounded off to the nearest cent. If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous and a benign tumor is a tumor that is harmless.

So obviously people care a lot about this. One of this size, one of this size. And so on. And sadly we also saw a few malignant tumors, one of that size, one of that size, one of that size… So on. So this example… I have five examples of benign tumors shown down here, and five examples of malignant tumors shown with a vertical axis value of one.

The machine learning question is, can you estimate what is the probability, what is the chance that a tumor is malignant versus benign? To introduce a bit more terminology this is an example of a classification problem. And it turns out that in classification problems sometimes you can have more than two values for the two possible values for the output. As a concrete example maybe there are three types of breast cancers and so you may try to predict the discrete value of zero, one, two, or three with zero being benign.

Benign tumor, so no cancer. And one may mean, type one cancer, like, you have three types of cancer, whatever type one means. And two may mean a second type of cancer, a three may mean a third type of cancer. But this would also be a classification problem, because this other discrete value set of output corresponding to, you know, no cancer, or cancer type one, or cancer type two, or cancer type three.

In classification problems there is another way to plot this data. Let me show you what I mean. Let me use a slightly different set of symbols to plot this data. Like so. I hope this is beginning to make sense.

cs 7641 final notes

All I did was I took, you know, these, my data set on top and I just mapped it down. To this real line like so. And started to use different symbols, circles and crosses, to denote malignant versus benign examples. Now, in this example we use only one feature or one attribute, mainly, the tumor size in order to predict whether the tumor is malignant or benign.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Reinforcement Learning: A Survey. Put an agent into a world make sure you can describe it with an MDP! Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. No description, website, or topics provided. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 6cb Jun 10, Choose features based on how the learner performs. Usefulness Relevance measures the effect the variable has on the Bayes' Optimal Classifier Usefulness measures the effect the variable has on the error of a particular predictor ANN, DT, etc. Feature Transformation Polsemy: Same word different meaning - False Positives Synonomy: Different word same meaning - False Negatives PCA: Good Slides Example of an eigenproblem Finds direction eigenvectors of maximum variance All principal components eigenvectors are mutually orthogonal Reconstructing data from the principal components is proven to have the least possible L2 squared error compared to any other reduction Eigenvalues are monotonically non-increasing and are proportional to variance along each principal component eigenvector.

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