EECS 649 Introduction to Artificial IntelligenceExamElectronic Blackboard Submission Due: April 24, 2019 @ 9PMPaper Copy Due: April 25, 2019 @ 4PM200 PointsDirections: You must read and follow these directions carefully. This is a 3 hour open notes,open internet exam. You may not collaborate or communicate with another student in the classor outside the class regarding the exam from 6pm, 4/24/2019 to 5 AM, 4/25/2019.In order to discourage cheating, if you can prove another student was asking you for help ordiscussing the exam with you prior to you or them submitting the exam and are the first to reportit, you will receive 50 extra points (not to exceed 200 points total) and they will have 50 pointssubtracted from their total). The person that attempted to cheat first will not be able to gain anypoints for reporting another cheating student but the reported cheating student will be deducted50 points. Any group attempt to maximize total or individual gains from any type of cheatingwill all receive zero points for their exam grade. Any detected cheating or plagiarism will resultin a zero points exam grade.There will be 5% grade reduction if the exam is turned in electronically after midnight and anadditional 5% grade reduction if turned in electronically after 2 AM. Exams will not be givenANY credit if submitted electronically after 5AM, April 25, 2019.No questions will be answered by the instructor during the exam. If you run into any issues, doyour best to describe your assumptions or any discrepancies and solve the problem.The exam answers that are not part of the programming portion should be submitted as a PDFfile. Clearly label the problem numbers, letters, and answers. Questions 1-4 should be typed(you may include diagrams if you wish). Question 5 can be submitted as a scanned PDF of ahandwritten answer since it involves “drawing” some diagrams. The programming portion(Question 4) should be submitted as a zip file containing all of the requested data and code.Make sure that your name is included at the top of each submitted page or file.TURN IN A PAPER COPY OF YOUR EXAM ANSWERS IDENTICAL TO YOURELECTRONICALLY SUBMITTED ANSWERS (MINUS THE DATA FILES) TO THE EECSOFFICE IN EATON 2001 BY 4PM ON APRIL 25, 2019. NO LATE PAPER COPIES WILLBE ACCEPTED.First Name: __________________________ Last Name: ___________________________21. [60 points] General Artificial Intelligence: Write a coherent and well organized one to twopage essay in paragraph form that explains what AI is, what it is not, and what are itslimitations or dangers. Be sure to include, explain, and clearly identify (e.g. number) thefollowing concepts: the history of AI, the present status of AI, intelligent agents and theirvarious architectures, problem solving as search, learning, environment characteristics of anintelligent agent, ethics, real-world examples, and list some of the subfields of AI. The realworld examples you provide should be from the in-class guest lectures. Be sure to give youressay a titl代写EECS 649作业、代做Artificial Intelligence作业、R课程设计作业代写、代做R编程作业 代e and adhere to spelling and grammar rules.2. [20 points] Logistic regression and deep learning: Briefly compare and contrast logisticregression and deep learning. Be sure to give definitions of each. Be sure to adhere tospelling and grammar rules.3. [20 points] Reinforcement learning: Briefly explain what reinforcement learning is and howdoes it relate to other methods of learning. Be sure to adhere to spelling and grammar rules.4. [80 points] Programming Machine Learning: Write a program in the language of your choice(e.g. R) to create a supervised learning model to predict the housing prices given the dataprovided on Blackboard (housetrain.csv, housetest.csv, and housedata_description.txt).Prepare the training set and test set to include only the following features: year and month ofsale, lot square footage, and number of bedrooms. housetrain.csv - the training set housetest.csv - the test set housedata_description.txt - full description of each columna. What is the particular supervised learning method you are using and why did youchoose it over other methods?b. What did you do with the data to prepare it for processing? [Rename your preppeddata to housetrain_prepped.csv and housetest_prepped.csv]c. How did you go about training your machine learning model (i.e. explain the stepby-stepprocess you used)?d. What are the set of features and the specific coefficient values that give you thebest results?e. What is your R2 value (i.e. r-squared value) and what does it mean?f. What is your RMSE value between the logarithm of the predicted value and thelogarithm of the observed sales price. (Taking logs means that errors in predictingexpensive houses and cheap houses will affect the result equally.)First Name: __________________________ Last Name: ___________________________3g. What is the predicted sales price values for id 1625 in the housetest.csv file ? Tofind the feature, or predictor, values for this problem, open the housetest.csv fileand look at the row that has id 1625. Use only the year and month of sale, lotsquare footage, and number of bedrooms in your learning model from thatexample.h. Graduate students only: Create another learning model and determine the set offeatures that provides the best result. List the features you identify and thecoefficients . State the R2 , RMSE values and compare them with the firstlearning model you created. Graduate student with the best performing modelwins “the prize.” Undergraduate students may do this portion for extra credit.i. Graduate students only: Write down the predicted sales prices for id 1625 intest.csv using your new learning model from part h . Undergraduate studentsmay do this portion for extra credit.j. Be sure to upload your R code and data to blackboard in a zip file labeled_machinelearningcode.zipYour R code and data should produce and display the results you describe in “a”through “i” above.First Name: __________________________ Last Name: ___________________________4转自：http://www.7daixie.com/2019042722062525.html
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