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<a id="nametitle" href="index.html#body">Ramviyas Nattamai Parasuraman<br />       <span class="subtitle">ASSISTANT PROFESSOR, <a class="External" href="http://www.cs.uga.edu">UGA CS</a></span></a>
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<h1 class=""><strong><font color="white">CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018)</font></strong> <br><br>
</div>
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<b>Instructor: <a class="External" href="index.html"> Ramviyas N. Parasuraman</a> </b>
<br> </p>
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<b>Time and Location of Lectures:</b>
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<p class="">
Monday (12:20-1:10 PM), Tuesday & Thursday (12:30-1:45 PM), Boyd #202 </p>
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<b>Office Hours and Location </b>
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<p class="">Tuesday & Thursday 2-3 PM or by an email appointment
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<b>Course Overview </b>
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<p class="">
Students completing this course will gain an understanding of the hardware and software involved in robotics, with a focus on programming algorithms. Students will learn various mathematical and statistical models, associated algorithms, and their implementations, which are making modern-day robotics possible.They will understand the past, present and future of robotics, and the main challenges that make robotics difficult.
<br> </p>
<p class="">
<strong>Textbook:</strong> "Introduction to Autonomous Mobile Robots" by Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, MIT Press (2nd Ed. 2011) <br>
<strong>Reference book:</strong> "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox. MIT Press (2005) <br>
<br> </p>
<p class="">
<strong>Note: </strong> We will follow the structure and content of the <strong><a href="https://www.edx.org/course/autonomous-mobile-robots" class="External">EdX course on Autonomous Mobile Robots</a></strong> and heavily use their slides in this class (all copyrights of the slides belong to the authors and EdX). It's an excellent course from the <strong><a href="http://www.ethz.ch/en.html" class="External">ETH Zurich</a></strong> team. The idea is that the student always has an additional venue for learning and reference. It also significantly helps the instructor in preparing course materials (slides).
<br> </p>
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<b>Course Topics </b>
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<p class="">
The course topics presented here is a general plan for the course; deviations announced to the class by the instructor may be necessary.<br>
<strong> I. Overview of Robotics:</strong> Introduction, Robot Hardware, Robotic Software Architectures, Probability Theory, Field Applications <br>
<strong> II. Robotic Perception:</strong> Range Finders and Camera models <br>
<strong> III. Robotic Motion:</strong> Kinematics, Velocity Motion Model, Odometry Motion Model, Motion and Maps <br>
<strong> IV. Localization:</strong> State Estimation under Uncertainty, Filters: Bayes, Kalman, Extended Kalman, and Monte Carlo, Localization methods: Markov, Extended Kalman Filter, Particle Filter, Monte Carlo framework, etc. <br>
<strong> V. Mapping:</strong> Occupancy Grid Mapping, Learning Inverse Measurement Model, Simultaneous Localization and Mapping (SLAM) solutions <br>
</p>
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<b>Grading </b>
</p>
</div>
</div>
<table width="200" class="table" >
<thead>
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<th><font color="yellow">Grading Item</font></th>
<th><font color="yellow">Percent</font></th>
</tr>
</thead>
<tbody>
<tr>
<td>Class participation</td>
<td>5%</td>
</tr>
<tr>
<td>Assignments</td>
<td>40%</td>
</tr>
<tr>
<td>Midterm exam</td>
<td>20%</td>
</tr>
<tr>
<td>Final exam</td>
<td>35%</td>
</tr>
</tbody>
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<br><br>
<p class="lead1 text-secondary">
<b>Class Schedule</b>
</p>
<table class="table">
<thead>
<tr>
<th><font color="yellow">Date</font></th>
<th><font color="yellow">Day</font></th>
<th><font color="yellow">Topics</font></th>
<th><font color="yellow">Comments</font></th>
</tr>
</thead>
<tbody>
<tr>
<!--<td>1</td>-->
<td>08-13-2018</td>
<td>Monday</td>
<td>Course Introduction</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture1/introduction-robotics.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture1/slides_from_EdX.pdf">Additional material from EdX - Introduction</a></td>
</tr>
<tr>
<td>08-14-2018</td>
<td>Tuesday</td>
<td>Overview of Robotics,<br />
History of Robotics,<br />
Sensors, and Hardware</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture2/introduction-robotics-lecture2.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture2/EdX_Sensors.pdf">Sensors and Perception (EdX)</a></td>
</tr>
<tr>
<td>08-16-2018</td>
<td>Thursday</td>
<td>Wheeled kinematics - Introduction <br /> ROS - Introduction</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture3/introduction-robotics-lecture3.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture3/ROS_introduction.pdf">Introduction to ROS</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture3/EdX_Wheeled_Kinematics_part1.pdf">Wheeled Kinematics - Part I (EdX)</a> </td>
</tr>
<tr>
<td>08-20-2018</td>
<td>Monday</td>
<td>ROS - Setup</td>
<td>Practicum <br><a style="color:yellow" href="docs/csx530_fall2018/lecture4/introduction-robotics-lecture4.pdf">Main slides</a> </td>
</tr>
<tr>
<td>08-21-2018</td>
<td>Tuesday</td>
<td>Kinematics - continued</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture5/introduction-robotics-lecture5.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture5/EdX_Kinematics_Basics.pdf">Basics of Kinematics (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture5/EdX_Wheeled_Kinematics_part2.pdf">Wheeled Kinematics - Part II (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture5/EdX_Wheeled_Kinematics_part3.pdf">Wheeled Kinematics - Part III (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture5/EdX_Wheeled_Kinematics_part4_differential_drive.pdf">Wheeled Kinematics - Differential Drive Robot (EdX)</a> </td>
</tr>
<tr>
<td>08-23-2018</td>
<td>Thursday</td>
<td>Probability theory - basics</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture6/introduction-robotics-lecture6.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture6/Basics_of_Probability.pdf">Basics of Probability in Robotics</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture6/EdX_Wheeled_Kinematics_degree_of_Maneuverability.pdf">Kinematics - Degree of Maneuverability (EdX)</a> </td>
</tr>
<tr>
<td>08-27-2018</td>
<td>Monday</td>
<td>Sensors Model - Rangefinders</td>
<td class="green">Guest Lecture by <a href="http://thinc.cs.uga.edu/personnel.html" class="External" target="_blank_">Prof. Prashant Doshi</a></td>
</tr>
<tr>
<td>08-28-2018</td>
<td>Tuesday</td>
<td>ROS: Nodes, Topics, Publisher, and Subscriber</td>
<td>Practicum</td>
</tr>
<tr>
<td>08-30-2018</td>
<td>Thursday</td>
<td>Sensors Model: cameras</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture9/introduction-robotics-lecture9.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture9/EdX_Vision_1_Introduction_Computer_Vision.pdf">Introduction to Computer Vision (EdX) </a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture9/EdX_Vision_2_Camera_image_formation_perspective_projection.pdf">Perspective Camera Model (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture9/EdX_Vision_3_Omnidirectional_projection_camera_calibration_unified_model.pdf">Omnidirectional Camera Model (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture9/EdX_Vision_4_Stereo_Vision.pdf">Stereo Camera Model (EdX)</a> </td>
</tr>
<tr>
<td>09-03-2018</td>
<td>Monday</td>
<td></td>
<td><span class="blue">Labor Day - Holiday (No class)</span><br /><span style="color:orange">Assignment 1 announced <a style="color:yellow" href="docs/csx530_fall2018/assignment1/csci_x530_robotics_assignment1.pdf">(Instructions)</a> - deadline 09/10 (12:30 pm) </span>
</td>
</tr>
<tr>
<td>09-04-2018</td>
<td>Tuesday</td>
<td>Perception: Computer Vision - fundamentals <br> ROS: Subscriber and Custom Messages</td>
<td>Lecture + Practicum <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture10/introduction-robotics-lecture10.pdf">Main slides</a>
</td>
</tr>
<tr>
<td>09-06-2018</td>
<td>Thursday</td>
<td>Perception: Image processing basics</td>
<td> <a style="color:yellow" href="docs/csx530_fall2018/lecture11/EdX_Computer_Vision_Correlation_Convolution.pdf">Image Processing - Correlation and Convolution (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture11/EdX_Computer_Vision_Edges_Points.pdf">Image Processing - Edges and Points (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture11/Homework_Structure_from_Motion.pdf">Homework - Structure from Motion (EdX)</a> </td>
</tr>
<tr>
<td>09-10-2018</td>
<td>Monday</td>
<td>ROS: Services and Parameters</td>
<td>Practicum <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture12/introduction-robotics-lecture12.pdf">Main slides</a>
</td>
</tr>
<tr>
<td>09-11-2018</td>
<td>Tuesday</td>
<td>Perception: Feature extraction, edge detection, corner detection, SIFT features, etc.</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture13/introduction-robotics-lecture13.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture13/EdX_Computer_Vision_Correlation_Example.pdf">Image Processing - Correlation Example (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture13/EdX_Computer_Vision_Edges_Points.pdf">Image Processing - Corner Detection and Feature Extraction (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture13/EdX_Computer_Vision_Place_recognition.pdf">Computer Vision - Object/Place Recognition (EdX)</a> </td>
</tr>
<tr>
<td>09-13-2018</td>
<td>Thursday</td>
<td>Computer Vision: Line Extraction <br> Localization: Introduction to Markovian Approach </td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture14/introduction-robotics-lecture14.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture14/EdX_Computer_Vision_Line_Extraction.pdf">Perception - Line Extraction (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture14/EdX_Localization_Markov_Approach.pdf">Localization - Markovian Approach (EdX)</a> </td>
</tr>
<tr>
<td>09-17-2018</td>
<td>Monday</td>
<td>ROS: Assignment 1 discussions, running ROS across different machines</td>
<td>Practicum <br /><span class="blue"></span></td>
</tr>
<tr>
<td>09-18-2018</td>
<td>Tuesday</td>
<td>Localization: Markovian and Kalman Filter Approaches</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture16/introduction-robotics-lecture16.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture16/EdX_Localization_Markov_Approach.pdf">Localization - Markovian Approach (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture16/EdX_Localization_Kalman_Filter_Approach.pdf">Localization - Kalman Filter Approach (EdX)</a> </td>
</tr>
<tr>
<td>09-20-2018</td>
<td>Thursday</td>
<td>Localization - Probabilistic map-based localization, Markov localization, Kalman filter localization</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture17/introduction-robotics-lecture17.pdf">Main slides</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture17/MarkovLocalization_Example_Prof.ChrisClark.pdf">Markov Localization Example (slides courtesy of Prof. Chris Clark, Princeton, 2011)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture17/EdX_Localization_Kalman_Filter_Approach.pdf">Localization - Kalman Filter Approach (EdX)</a> </td> </tr>
<tr>
<td>09-24-2018</td>
<td>Monday</td>
<td>ROS: rosbag and working with simulated data <br> Example implementation of EKF localization in Python</td>
<td>Practicum <br><a style="color:yellow" href="docs/csx530_fall2018/lecture18/introduction-robotics-lecture18.pdf">Main slides</a> </td>
</tr>
<tr>
<td>09-25-2018</td>
<td>Tuesday</td>
<td>Localization: Particle Filters</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture19/introduction-robotics-lecture19.pdf">Main slides</a>
</td>
</tr>
<tr>
<td>09-27-2018</td>
<td>Thursday</td>
<td>Localization: Examples </td>
<td></td>
</tr>
<tr>
<td>10-01-2018</td>
<td>Monday</td>
<td>ROS: Simulation in Gazebo</td>
<td>Practicum <br><a style="color:yellow" href="docs/csx530_fall2018/lecture21/introduction-robotics-lecture21.pdf">Main slides</a> </td>
</tr>
<tr>
<td>10-02-2018</td>
<td>Tuesday</td>
<td>SLAM: Introduction to the SLAM problem, challenges</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture23/EdX_SLAM.pdf">Introduction to SLAM Problem (EdX)</a></td>
</tr>
<tr>
<td>10-04-2018</td>
<td>Thursday</td>
<td><span class="blue">Mid-term exam (theory). <br> Practical Mid-term project will be annouced soon </span></td>
<td></td>
</tr>
<tr>
<td>10-08-2018</td>
<td>Monday</td>
<td>Discussion of Midterm questions and solutions </td>
<td><span style="color:orange">Midterm project announced <a style="color:yellow" href="docs/csx530_fall2018/midterm/midterm_project.pdf">(Instructions)</a> - Deadline 10/15 12:30 pm </span></td>
</tr>
<tr>
<td>10-09-2018</td>
<td>Tuesday</td>
<td>SLAM: continued</td>
<td> <a style="color:yellow" href="docs/csx530_fall2018/lecture23/EdX_SLAM.pdf">Introduction to SLAM Problem (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture23/EdX_SLAM_Example.pdf">SLAM Problem - Illustration (EdX)</a> <br> <a style="color:yellow" href="docs/csx530_fall2018/lecture23/EdX_Monocular_SLAM.pdf">SLAM - Using Monocular Vision (EdX)</a> </td>
</tr>
<tr>
<td>10-11-2018</td>
<td>Thursday</td>
<td>SLAM- continued</td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture24/EdX_SLAM_MonocularSLAM_Challenges.pdf">SLAM - Challenges and Beyond (EdX)</a> </td>
</tr>
<tr>
<td>10-15-2018</td>
<td>Monday</td>
<td>Machine Learning</td>
<td><span class="green">Guest Lecture by <a href="http://cobweb.cs.uga.edu/~shengli" class="External" target="_blank_">Prof. Sheng Li</a></span> <br>
<a style="color:yellow" href="docs/csx530_fall2018/guest/CSCI_4530_Guest_Lecture_Machine_Learning.pdf">Machine Learning - Prof. Sheng Li</a>
</td>
</tr>
<tr>
<td>10-16-2018</td>
<td>Tuesday</td>
<td>Cloud Computing and IoT</td>
<td><span class="green">Guest Lecture by <a href="http://cobweb.cs.uga.edu/~kim" class="External" target="_blank_">Prof. In Kee Kim</a></span> </td>
</tr>
<tr>
<td>10-18-2018</td>
<td>Thursday</td>
<td>Demonstration of mid-term projects</td>
<td></td>
</tr>
<tr>
<td>10-22-2018</td>
<td>Monday</td>
<td>ROS - rosparameters, joy teleop, laser scan data</td>
<td>Practicum </td>
</tr>
<tr>
<td>10-23-2018</td>
<td>Tuesday</td>
<td>SLAM - Recap and Conclusion <br>
Navigation and Motion Planning - Introduction and Collision Avoidance </td>
<td><span style="color:orange">Assignment 2 announced <a style="color:yellow" href="docs/csx530_fall2018/assignment2/csci_x530_robotics_assignment2.pdf">(Instructions)</a> - deadline 10/30 (12:30 pm) <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture26/EdX_MotionPlanning_Introduction.pdf">Introduction to Motion Planning (EdX)</a> <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture26/EdX_MotionPlanning_CollisionAvoidance.pdf">Motion Planning - Collision Avoidance using Velocity Obstacles Methods (EdX)</a>
</span>
An Example implementation of RVO algorithm for Multi-Agent Systems by Dr. Meng Guo is available <a href="https://github.com/MengGuo/RVO_Py_MAS"> here </a>
</td>
</tr>
<tr>
<td>10-25-2018</td>
<td>Thursday</td>
<td>Navigation and Motion Planning - Collision Avoidance using Potential Fields </td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture27/EdX_MotionPlanning_PotentialFieldMethods.pdf">Motion Planning - Potential Field Methods (EdX)</a></td>
</tr>
<tr>
<td>10-29-2018</td>
<td>Monday</td>
<td> ROS transform frames (tf) - static and dynamic transform <br> tf broadcaster and listener example in Python </td>
<td>Practicum</td>
</tr>
<tr>
<td>10-30-2018</td>
<td>Tuesday</td>
<td>Navigation and Motion Planning - Potential Fields (contd.) <br>
Motion Planning - Graph Construction and Graph-based Search methods - Dijkstra, A*, RRT algorithms </td>
<td>
<a style="color:yellow" href="docs/csx530_fall2018/lecture28/EdX_MotionPlanning_PotentialFields_Example.pdf">Potential Fields - Example (EdX)</a> <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture28/EdX_MotionPlanning_GraphSearch_Part1.pdf">Motion Planning - Graph Construction (EdX) <br>
<a style="color:yellow" href="docs/csx530_fall2018/lecture28/EdX_MotionPlanning_GraphSearch_Part2.pdf">Motion Planning - Graph-based Search Methods(EdX)
</td>
</tr>
<tr>
<td>11-01-2018</td>
<td>Thursday</td>
<td>Navigation and path planning - A* Algorithm Example and Conclusion </td>
<td> <a style="color:yellow" href="docs/csx530_fall2018/lecture29/EdX_MotionPlanning_AStarExercise.pdf"> A* - Example (EdX)</a> </td>
</tr>
<tr>
<td>11-05-2018</td>
<td>Monday</td>
<td>ROS - actionlib, working with real robots</td>
<td>Practicum</td>
</tr>
<tr>
<td>11-06-2018</td>
<td>Tuesday</td>
<td>Robot Motion Models - Odometry and Velocity Models </td>
<td><a style="color:yellow" href="docs/csx530_fall2018/lecture30/RobotMotionModels.pdf"> Motion Models </a></td>
</tr>
<tr>
<td>11-08-2018</td>
<td>Thursday</td>
<td>ROS Examples - SLAM <br> Discussion on Final Project </td>
<td> Practicum <br> Discussion </td>
</tr>
<tr>
<td>11-12-2018</td>
<td>Monday</td>
<td>ROS Examples - Transfer Frames (TF) <br> Robot Applications in Radioactive Environments - Examples </td>
<td> Practicum <br> Discussion </td>
</tr>
<tr>
<td>11-13-2018</td>
<td>Tuesday</td>
<td>ROS Practical - Hector SLAM </td>
<td>Practicum <br>
<span style="color:orange">Assignment 3 announced <a style="color:yellow" href="docs/csx530_fall2018/assignment3/csci_x530_robotics_assignment3.pdf">(Instructions)</a> - deadline 11/20 (11:30 pm) </td>
</tr>
<tr>
<td>11-15-2018</td>
<td>Thursday</td>
<td>Robot Planning - Markov Decision Process (MDP) </td>
<td>I used Prof. Andrew Vardy's <a style="color:yellow" href="http://www.cs.mun.ca/~av/courses/4766-current/manual_uploads/probPlanning_printout.pdf"> slides </a> (with permission)</td>
</tr>
<tr>
<td>11-19-2018</td>
<td>Monday</td>
<td>-</td>
<td><span class="blue">Thanksgiving Break</span></td>
</tr>
<tr>
<td>11-20-2018</td>
<td>Tuesday</td>
<td>-</td>
<td><span class="blue">Thanksgiving Break</span></td>
</tr>
<tr>
<td>11-22-2018</td>
<td>Thursday</td>
<td>-</td>
<td><span class="blue">Thanksgiving Break</span></td>
</tr>
<tr>
<td>11-26-2018</td>
<td>Monday</td>
<td> Discussion on Final Project - Status updates </td>
<td></td>
</tr>
<tr>
<td>11-27-2018</td>
<td>Tuesday</td>
<td>Recap of all topics and conclusion of the course</td>
<td>Conclusion</td>
</tr>
<tr>
<td>11-29-2018</td>
<td>Thursday</td>
<td>Discussion of Assignment 3 solutions and Final Project updates </td>
<td></td>
</tr>
<tr>
<td>12-03-2018</td>
<td>Monday</td>
<td>Scheduled demonstration of final project </td>
<td>All teams</td>
</tr>
<tr>
<td>12-04-2018</td>
<td>Tuesday (Friday in Effect)</td>
<td>-</td>
<td>No class because Friday schedule </td>
</tr>
<tr>
<td>12-10-2018 </td>
<td>Monday</td>
<td>Final Exams (written) - 12 to 3 pm at Boyd 202 </td>
<td>Final Exam</td>
</tr>
<tr>
<td>12-17-2018</td>
<td>Monday, 12 PM</td>
<td>Grades due</td>
<td></td>
</tr>
</tbody>
</table>
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