Cs229 final exam

17.04.2021

A final examinationannualexamfinal interviewor simply finalis a test given to students at the end of a course of study or training. Although the term can be used in the context of physical training, it most often occurs in the academic world. Most high schoolscollegesand universities run final exams at the end of a particular academic termtypically a quarter or semesteror more traditionally at the end of a complete degree course.

The purpose of the test is to make a final review of the topics covered and assessment of each student's knowledge of the subject. A final is technically just a greater form of a "unit test". They have the same purpose, finals are simply larger. Not all courses or curricula culminate in a final exam; instructors may assign a term paper or final project in some courses.

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The weighting of the final exam also varies. It may be the largest—or only—factor in the student's course grade; in other cases, it may carry the same weight as a midterm exam, or the student may be exempted. Not all finals need be cumulative, however, as some simply cover the material presented since the last exam. For example, a microbiology course might only cover fungi and parasites on the final exam if this were the policy of the professor, and all other subjects presented in the course would then not be tested on the final exam.

Prior to the examination period most students in the Commonwealth have a week or so of intense revision and study known as swotvac. In the UKmost universities hold a single set of "Finals" at the end of the entire degree course.

In Australiathe exam period varies, with high schools commonly assigning one or two weeks for final exams, but the university period—sometimes called "exam week" or just "exams"—may stretch to a maximum of three weeks. Practice varies widely in the United States ; "finals" or the "finals period" at the university level constitutes two or three weeks after the end of the academic term, but sometimes exams are administered in the last week of instruction.

Some institutions designate a "study week" or "reading period" between the end of instruction and the beginning of finals, during which no examinations may be administered. Students at many institutions know the week before finals as " dead week. Though common in French tertiary institutionsfinal exams are not often assigned in French high schools.

In some countries and locales that hold standardised exams, it is customary for schools to administer mock examinationswith formats modelling the real exam. Students from different schools are often seen exchanging mock papers as a means of test preparation. A take-home final is an examination at the end of an academic term that is usually too long or complex to be completed in a single session as an in-class final.

There is usually a deadline for completion, such as within one or two weeks of the end of the semester. A take-home final differs from a final paper, often involving research, extended texts and display of data.

In some cases, schools will run on a modified schedule for final exams to allow students more time to do their exams.

cs229 final exam

However, this is not necessarily the case for every institution. From Wikipedia, the free encyclopedia. Assessment at the end of a course or training to gauge one's mastery. For other uses, see Final exam disambiguation. For the horror film, see Final Examination film. This article does not cite any sources.

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Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Authority control GND : Categories : School terminology Examinations. Hidden categories: Use American English from January All Wikipedia articles written in American English Articles with short description Use mdy dates from January Articles lacking sources from June All articles lacking sources All articles with unsourced statements Articles with unsourced statements from June Wikipedia articles with GND identifiers.

Namespaces Article Talk.Object-oriented programming experience using a language suitable for exploring advanced topics in programming. Topics include memory management, parameter passing, inheritance, compiling, debugging, and maintaining programs. Significant programming projects. The course emphasizes differences between these languages and Java, and focuses on memory management and other issues.

The programming projects will, in fact, be "significant". More on these, below. At the end of the semester, students should understand differences between managed languages e. If you have a disability that may require some modification of seating, testing, or other class requirements, please obtain a SAAR Student Academic Accommodation Request form verifying your disability and specifying the accommodation you will need from the Disability Resources staff and present it to the instructor as soon as possible so that appropriate arrangements may be made.

Course prerequisites are ComS and credit or enrollment in Mathor permission of the instructor. No exceptions. These will be discussed in class and posted on Bb Learn. A few words of warning about the programming projects, in no particular order. These are individual projects spanning several weeks. Start Early! Starting on a project the weekend before the due date is a common way to fail or drop this course.

Your code will be tested on one of the department's Linux machines, probably pyrite. Your code must compile and run correctly on this machine. Code that does not compile may receive a grade of zero points!

Machine Learning

The projects will require one or two thousand of lines of code to implement give or take; your mileage may vary. These should be organized into more than one source file, with the appropriate supporting documentation a README file.

cs229 final exam

Part of your grade will be on programming style: code should be legible and well-documented. You must use a Makefileso that all your executables may be built by simply typing make in the shell.

cs229 final exam

You are responsible for understanding the project specifications. These are written in natural language Englishand may be vague or contradictory it is not easy to describe what a thousand lines of code should do. There will be an online discussion forum for the course, where students may clarify any questions about the specifications.

This is another reason to start early. Exams are closed textbook and closed notes, and computing devices are not allowed during exams. Warning: you will have to write code on the exams. There will be one midterm examscheduled for 15 October and held in our usual classrom at the usual time.

There will be a 2-hour, in-class, comprehensive final examheld in our usual room on Monday, December 16 from pm until our University-scheduled time slot. Homework solutions including projects are due by pm on their due date, and will be subject to a penalty based on the number of days late, as follows.

Exams are due at the specified time usually, the end of the exam slot and will not be accepted late.This course provides a broad introduction to machine learning and statistical pattern recognition. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Students are expected to have the following background: Prerequisites: - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Stat is sufficient but not necessary. Ng's research is in the areas of machine learning and artificial intelligence. Since its birth inthe AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing.

This is in distinct contrast to the year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI. Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself.

Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute.

As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles. Stanford University. CS - Machine Learning. Course Details Show All. Course Description. Ng, Andrew.

Course Handouts. Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Previous projects: A list of last year's final projects can be found here. For emacs users only: If you plan to run Matlab in emacs, here are matlab.

The official documentation is available here.A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc.

Written homework assignments focus on various concepts; additionally, students choose either a take-home final exam or a series of programming assignments geared towards neural network creation, training, and inference. This course replaces A and satisfies all similar requirements.

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Lippman told me one day, since the experimentalists believe that it is a mathematical theorem, and the mathematicians that it is an experimentally determined fact. Home Staff Lectures Assignments. Summary A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. A Motivational Thought "Everyone is sure of this [that errors are normally distributed], Mr.

TA sections : Fridays, am to pm, Nvidia Auditorium. Prerequisites : Math 51; Math or or equivalent or comfort with the associated material. This course will be recordedand attendance is encouraged but not required. Note that the SCPD team has asked us to include the following information regarding video recordings: Video cameras located in the back of the room will capture the instructor presentations in this course.

For your convenience, you can access these recordings by logging into the course Canvas site. These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes.

Note that while the cameras are positioned with the intention of recording only the instructor, occasionally a part of your image or voice might be incidentally captured. If you have questions, please contact a member of the teaching team. Only 4 questions are needed to get a full score. See the assignments section for more information. To ensure fairness, each option will be evaluated separately.You have collected a dataset of their scores on the two exams, which is as follows:.

You'd like to use polynomial regression to predict a student's final exam score from their midterm exam score. Further, you plan to use both feature scaling dividing by the "max-min", or range, of a feature and mean normalization. What is the normalized feature x 2 4? Please round off your answer to two decimal places and enter in the text box below.

cs229 final exam

Based on this, which of the following conclusions seems most plausible? Should you prefer gradient descent or the normal equation? Answer to question 2 is wrong. Of course you would get a more accurate answer in terms of more decimal points, but one has to see a tradeoff between speed and efficiency of the program to that of accuracy.

So the current answer given holds, but I can see your point. Skip to content.

Computer Science 229

Instantly share code, notes, and snippets. Code Revisions 1 Stars 3. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. You have collected a dataset of their scores on the two exams, which is as follows: Midterm Exam midterm exam 2 Final Exam 89 96 72 74 94 87 69 78 You'd like to use polynomial regression to predict a student's final exam score from their midterm exam score.

Answer: The mean of x 2 is The normal equation, since it provides an efficient way to directly find the solution.Please use Piazza to ask questions you have throughout the course. Use access code vision. Here is the link to the repository of notes.

Assignment 0 is a simple assignment to get you acquainted with python and basic libraries we will be using in the course. You will have one week to complete every assignment but all the assignments will be available 2 weeks before they are due.

It will be due on Fridays at pm. The final exam will be a 3 hour exam where you will be tested on the concepts, applications and theories taught in the class. Throughout the course you will have opportunities to earn extra credit. They can be earned by completing the extra credit questions in every assignment or by improving the existing course notes.

These extra credit points are designed to allow students to delve deeper into the topics that they find most interesting. You can improve your final class grade by 1 letter point using extra credit, i.

The course staff believes that the best way to learn something is to teach it to someone else. Since we have changed the class last year and its assignments from previous years, we ask that all the students contribute to help teach one another by contributing to the class notes. You can choose to add to the existing notes from last year.

There are new materials that have been added this year that don't have any notes, meaning that there are plenty of opportunities to earn extra credit. The class notes will be graded based on fluency grammatically correct complete English sentencesconsistency the entire lecture should be a coherent messagecoverage all topics in the lecture are included.

You will have a total of 7 late days that you can use in whichever assignments you prefer. There is a limit of 3 late days used per assignment, which means that the hard deadline for each assignment is on Monday at pm. All class assignments will be in Python with numpy. Please review this NumPy tutorial to help with your assignments. Linear Algebra e. MATH 51 We will use matrix transpose, inverse, rotation, translation and other algebraic operations with matrix expressions.Time and Location : Monday, Wednesday pmpm, links to lecture are on Canvas.

Note : This is being updated for Spring The dates are subject to change as we figure out deadlines. Please check back soon. Linear Regression. Logistic Regression.

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Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. Naive Bayes. Laplace Smoothing. Support Vector Machines. GMM non EM. Expectation Maximization.

CS229: Machine Learning

Class Notes Unsupervised Learning, k-means clustering. Value Iteration and Policy Iteration. Value function approximation. Previous projects: A list of last quarter's final projects can be found here. Data: Here is the UCI Machine learning repositorywhich contains a large collection of standard datasets for testing learning algorithms. Slides Introduction slides [ pptx ] Introduction slides [ pdf ].

Lecture 16 - Independent Component Analysis \u0026 RL - Stanford CS229: Machine Learning (Autumn 2018)

Problem Set 0. Weighted Least Squares. Class Notes Live lecture notes [ pdf ]. Problem Set 1. Class Notes Generative Algorithms [ pdf ] Live lecture notes [ pdf ]. Notes Section slides [ pdf slides ] Jupyter notebook [ html ][ source ]. Class Notes Deep Learning [ pdf ] Backpropagation. Problem Set 2. Notes Evaluation Metrics [ pdf slides ].

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Class Notes Regularization and Model Selection [ pdfaddendum ] Live lecture notes [ draft ] Double Descent [ linkoptional reading]. Notes Deep Learning [ pptx ]. Problem Set 3. Class Notes Midterm review [ pdf slides ]. Class Notes Factor Analysis [ pdf ] Live lecture notes [ draftin lecture ].

See details at Piazza post.