Recommend taking CSE 566 with him as well! CSE 215: Foundations of Computer Science – Midterm Exam 1 Study Exercises. Start studying CSE 2123 Midterm II. 6. With that said, if you do well on everything else in the course, it is easy to get a B. I got a B+ with minimal effort and I bombed the midterm (50%). He's passionate about the material and makes it easy for students to learn the important concepts. Quiz 1 worth 2% of your entire grade. CSE 132, CSE 240, CSE 247, or permission of the instructor. The problems in this exam are not necessarily ordered by … The only hard part about this class was the midterm and the final. Serving the Learning needs of a Changing Community. Learn vocabulary, terms, and more with flashcards, games, and other study tools. The message board will remain open for questions. Time/Location: Monday/Wednesday 2:30–4pm, Steinberg 105 Knowledge of Python. Please ask all questions on Piazza! Overall, the content and difficulty of the coursework did not seem to match that of the final. You may use a calculator 4. CSE 114 Midterm 2 Fall 2017 12. Office hours (TA): Fridays 12–4pm, Jolley Hall 408 Evaluation is based on programming assignments, a midterm exam, and a final exam. View Full Announcement on Ed. We're hiring! CSE 141 Midterm Exam 2011 Winter Professor Steven Swanson 1. (10 points) Complete the tripleCut() method, which manipulates a deck (array) of PlayingCards (assume that the PlayingCard class is de ned elsewhere). Answering this set of questions should be just one part of your preparation for the exam. All TA office hours will be cancelled from February 12 through February 18 to allow time for simulated midterm meetings. CSE 142: Computer Programming I, Winter 2021 Instructor: Stuart Reges (reges@cs.washington.edu), CSE2 305: Tue 1-3 View CSE-511-Syllabus-Fall-2020.pdf from CSE 511 at Santa Clara University. Knowledge of Python. Programming exercises concretize the key methods. I'm planning on 8-10 hours for the rest as the project peaks. If you are unsure about your python skills, here are some nice tutorials: Some basic knowledge of statistics, probability theory and first order logic is recommended (a review lecture will be added if necessary). The course targets graduate students and advanced undergraduates. Department of Computer Science and Engineering 3115 Engineering Building College of Engineering Michigan State University East Lansing, MI 48824-1226. February 10: TA office hours cancelled for simulated midterm. … Knowledge of Python. ), Upload your own images to be visualized as "deep dreams", tutorial for the CS 188 AI course at Berkeley. Office Hours: Monday 3pm-4:30pm, Location: Jolley 508. Course Syllabus - Fall 2020 Data Processing at Scale (CSE 511) Instructors: Professor Samira Ghayekhloo Teaching Another good reference for reinforcement learning is Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. Review all Class Notes and Exercises in Chapters 1 - 4 in the textbook with your study group. Artificial Intelligence E81 CSE 511A When and Where Tuesday, Thursday, 2:30PM-4:00PM in McDonnell Hall 162. Definitely the best professor I have taken at ASU. CSE 102 CSE 102. 2. share. Important Note: Ask all your questions on Piazza rather than emailing the Professor/TA. Prerequisites. CSE midterm 1 study guide by TMahdi includes 69 questions covering vocabulary, terms and more. No outside material may be used. 3. Please write your name at the top of each page 2. CSE 11 Quizzes and Exams Reviews No calculators or electronic devices allowed in Quizzes and Exams. The final will be December 17, 3:30PM - 5:30PM. This is a classic textbook and highly recommended! No … The content is getting more advanced this week, but the first 3 weeks and the midterm are all the same as what's covered in 412 and 510. TAs: Nicole Wang, Zhihan Li, Jiahao Li, Tiancheng He Instructor: Professor Roman Garnett The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. CSE 511: Advanced Algorithms CSE 521: Computational Complexity CSE 531: Parallel Algorithms CSE 541: Formal Language and Automata Theory CSE 551: Graph Theory CSE 561: Cryptography. Good luck! Programming exercises will concretize the key methods. This will be critical to complete the programming assignments. Quizlet flashcards, activities and games help you improve your grades. Group 2: Computer Networks & Systems CSE 512: Distributed Database Systems CSE 522: Distributed Operating Systems CSE 532: Advanced Computer Architecture Piazza message board The midterm is October 14th, during class. In a triple cut, two positions (index values) are speci ed, and the deck is divided into three groups: CSE 114: Computer Science I – Practice Midterm Exam. Please note that the exam will include questions on material not covered by these practice exercises. CSE 511 - Data Processing at Scale Project Phase 2 Requirement Requirement In Project Phase 2, you need to write two User Defined Functions ST_Contains and ST_Within in SparkSQL and use them to do four spatial queries: Range query: Use ST_Contains. Please enter the site through the appropriate link below: Contact Information. This course is based on the CS 188 course at UC Berkeley. This course is an introduction to the field, with special emphasis on sound modern methods. You will get more partial credit that way. Prerequisites: CSE 247, ESE 326, Math 233 Lansing Community College exists so that all people have educational and enrichment opportunities to improve their quality of life and standard of living. Office hours (TA): Mondays 4–5:30pm, Jolley Hall 408 x Write all answers neatly in the space provided. CSE 132, CSE 240, CSE 247, or permission of the instructor. The required book for this course is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. The book is available online here. Directions:Below is a set of exercises you should do for practice. You may find lectures, slides, and more there. Answers written on this question packet will NOT be graded. Located in Lansing, MI, the capital of Michigan If you do not know Python, or are rusty, you may find some resources to help below. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, machine learning (decision trees, neural nets, reinforcement learning, and genetic algorithms) and machine vision. Programming exercises will concretize the key methods. Given a query rectangle R and a set of points P, find all the points within R. Range join query: Use ST_Contains. If you are unsure about any of these, please speak with the instructor. The course targets graduate students and advanced undergraduates. Office hours (Garnett): Wednesdays after class, Steinberg 105 Show your work. CSE 132, CSE 240, and CSE 241, or permission of the instructor. Evaluation is based on programming assignments, a midterm exam, and a final exam. Report Save. Evaluation is based on written and programming assignments, a midterm exam and a final exam. TA: Chip Schaff Where Success Begins! Interested in being a TA for CSE 142 or 143? Some basic knowledge of statistics, probability theory, and first-order logic. x Do not start the exam until directed to do so. Office hours (TA): Tuesdays 4–6pm, Jolley Hall 408 Either the second or third edition is fine. Evaluation is based on programming assignments, quizzes, a midterm exam, and a final exam. If you are unsure about any of these, please speak with the instructor. The course targets graduate students and advanced undergraduates. Email: FirstInitial LetterB LastName@wustl.edu This is a close book, closed notes exam. CSE 101 Midterm 1 (Practice Version) KEY Please place ALL of your nal answers on the answer sheet that you were given at the start of the exam. 5. If you are unsure about any of these, please speak to the instructor. The course targets graduate students and advanced undergraduates. Take notes and study a lot! Prerequisites. You can find autograder information here. level 2. Name: _____ Stony Brook ID #: _____ Directions: x You have 80 minutes to complete this exam. x Do not separate exam sheets. Pieter Abbeel giving the introductory lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the introductory lecture for the Fall 2013 Berkeley CS 188 course, Pieter Abbeel giving the uninformed search lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the uninformed search lecture for the Fall 2013 Berkeley CS 188 course, Pieter Abbeel stepping through some DFS and BFS examples, Dan Klein giving the informed search lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the informed search lecture for the Fall 2013 Berkeley CS 188 course, Pieter Abbeel stepping through some A* Search examples, Dan Klein giving the first constaint satisfaction problem lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the first constraint satisfaction problem lecture for the Spring 2013 Berkeley CS 188 course, Dan Klein giving the second constaint satisfaction problem lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the second constraint satisfaction problem for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the adversarial search lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the adversarial search lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel stepping through some alpha–beta pruning examples, Dan Klein giving the expectimax lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the expectimax lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the MDPs I lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the MDPs I lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the MDPs II lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the MDPs II lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the reinforcement learning I lecture for the Fall 2013 Berkeley CS 188 course, Pieter Abbeel giving the reinforcement learning I lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the reinforcement learning II lecture for the Fall 2012 Berkeley CS 188 course, Pieter Abbeel giving the reinforcement learning II lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel giving the probability lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel giving the Markov models lecture for the Spring 2014 Berkeley CS 188 course, Dan Klein giving the hidden Markov models lecture for the Fall 2013 Berkeley CS 188 course, Pieter Abbeel giving the particle filter/HMM applications lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel giving the Bayes nets I lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel giving the Bayes nets II lecture for the Spring 2014 Berkeley CS 188 course, Pieter Abbeel working through some D-separation examples, Pieter Abbeel giving the decision diagrams/VPI lecture for the Spring 2014 Berkeley CS 188 course, Nicholas Hay giving the naive Bayes lecture for the Spring 2014 Berkeley CS 188 course, SethBling's MarI/O video: follow-up (MarI/O learns a one-frame speedrunning trick! If you have any questions, please raise your hand. syllabus CSE 132, CSE 240, and CSE 347, or permission of the instructor. Reading: Russel/Norvig, Chapter 1, Sections 26.3 and 27.4, Reading: Russel/Norvig, Chapter 3, Sections 1–4, Reading: Russel/Norvig, Chapter 3, Sections 5–6, Reading: Russel/Norvig, Sections 5.2–5 and 16.1–16.3, Reading: Russel/Norvig, Sections 17.1–3, Sutten and Barto Chapters 3–4 (see below), Reading: Russel/Norvig, Chapter 21, Sutten and Barto Sections 6.1–2, 6.5 (see below), Reading: Russel/Norvig, Chapter 13 Sections 1–5, Reading: Russel/Norvig, Chapter 15 Sections 2, 5, Reading: Russel/Norvig, Chapter 15 Sections 2, 6, Reading: Russel/Norvig, Chapter 14 Sections 1–2, 4, Reading: Russel/Norvig, Chapter 16 Sections 5–6, Reading: Russel/Norvig, Chapter 20 Sections 1–2, Code: Google released code to implement "deep dreaming", There is an interactive lesson plan available on, The Washington University library has electronic copies of the. Took the class online: 5 Assignments, 1 Easy Midterm and 1 VERY HARD Final. CSE 511: Data Processing at Scale.