|Lectures:||Tu, Th 12:30 - 1:45 pm in CAS 313|
|Labs:||Fri 10:10-11, 12:20-1:10, and 3:35-4:25 in EMA 304|
|Instructor:||Prof. Margrit Betke|
|Teaching Fellow:||Ajjen Joshi|
|Class web page:
|Class mailing list:
||firstname.lastname@example.org and email@example.com|
|Margrit Betkefirstname.lastname@example.org||353-6412||Tue 2-3:30, Thu 3-4:30 and by appointment||MCS 286|
|Ajjen Joshiemail@example.com||Please email||Mon 4-6 and by appointment||Ugrad Lab|
Seeing Me in My Office:
Please feel free to stop by my office anytime. My office is in MCS 286 (111 Cummington St). I am generally around every day. I may be in meetings, so the best time to reach me is during office hours. If you have classes during my office hours, speak to me after our lecture to make an appointment that works for both of us. I'm happy to talk with you about the course, research in AI, computer vision, machine learning, and pattern recognition, your plans for the future, or anything else. Check out my just updated personal web page to get to know me a little.
Responsibilities of the Teaching Fellow:
Ajjen is responsible for teaching three laboratory sections and helping you out during his office hours. He will also help me design the written homework and programming projects, and he will manage the graders. Please contact him first if you have questions about your homework grades.
Our goal is to learn about computer systems that exhibit intelligent behavior, in particular, perceptual and robotic systems. Topics include human computer interfaces, computer vision, robotics, machine learning, game playing, knowledge representation, planning, pattern recognition, and natural language processing.
Prerequisites of CS440: Geometric Algorithms (CAS CS 132) or Linear Algebra (CAS MA 242), 1 Year Programming Experience (CS 112 level, Python, Java, or C++) or consent of instructor. Prerequisites of CS640: Same as above and BA background in Computer Science (e.g., Algorithms, Theory, Programming Languages).
Handouts: The updated course syllabus and most handouts are made available online. Check our course web page at least once a week for homework assignments and other information.
Textbook: You are not required to buy a text book. I will hand out course materials in class and direct you to additional materials on the web (e.g., wikipedia). For background reading, you may go to the Science Library on Cummington St. I requested that two books are placed on reserve for you there: Artificial Intelligence by Patrick H. Winston (barnesandnoble and amazon) and Artificial Intelligence A Modern Approach by Russell and Nordig, Edition 2 or Edition 3. Both books provide a fundamental background of AI. I will follow, for example, the chapters about Expert Systems and Neural Nets in the Winston book and about Robotics in the Russell and Nordig book, and then augment the materials with newer research papers.
Computing Environment: You will use the Computer Science Department's main server csa2.bu.edu to submit programming solutions. To get an account on csa2, go to the Computer Science Department's Undergraduate Lab located at 730 Commonwealth Ave. You can work on various platforms in the lab there (and have immediate access to the computing staff and TF help). You can also access csa2 remotely using scp and ssh (linux) or putty and WinSCP (Windows).
Class Participation: Come to class and participate regularly. Reading the assigned texts and listening in class will only give you a "passive understanding" of the material. I encourage discussions in class to help you acquire an "active understanding" of the material so that you can evaluate existing computer systems critically and learn to develop your own creative solutions.
Reading: To prepare for each class, you will be asked to read textbook sections, wikipedia pages and journal papers, and explore web sites. You can achieve a good understanding and appreciation of the state-of-the-art in artificial intelligence if you read the assigned texts thoroughly.
Homework: The homework includes three programming
assignments and three written problem sets. The due dates are listed
below. Programs and reports must be submitted electronically. Guidelines for
submission are provided with each assignment. Written homework must be handed
in at the beginning of class.
Late solutions will be levied a late penalty of 20% per day (up to three days). After three days, no credit will be given.
Project: This year, 16 students are enrolled in CS 640. A project is only required for students enrolled in CS640. The project involves developing a system for lung cancer detection and participating in the Data Science Bowl 2017. Ajjen and I will discuss your project's scope, design, and presentation with you in class, labs, and office hours. Read the competition guidelines and the tutorial carefully. You will present the project in class at the beginning of April.
Colloquia: Students enrolled in CS440 and CS 640 are encouraged to attend the CS Department Seminars. Faculty search talks, which will be announced only by email to firstname.lastname@example.org, will typically be on M/W/F at 11 am. The Image and Video Computing research group will have seminars on Mondays at 1 pm (Room MCS 148). You may also check out AI talks in other departments and at other universities. A partial list includes: BU College of Engineering Seminar Calendar, CSAIL lab at MIT, MIT Department of Electrical Engineering and Computer Science, Northeastern College of Computer and Information Science, Tufts Department of Computer Science Colloquium, and UMass Boston Department of Computer Science.
Students enrolled in CS 640 must attend at least three talks on subjects related to Artificial Intelligence and write a summary on each talk. The one-page review should give a problem definition, summarize the algorithms and results, discuss the work critically, and also briefly explain how the work relates to material discussed in class. You must submit at the beginning of class on the dates listed in the syllabus. Check your text for typographical and grammatical errors. You will lose points if you simply copy the speaker's abstract, or if your review is late, or contains errors.
Exams: There will be two exams on the material discussed in the class and practiced with homeworks. The exams will be quite easy for students who come to class, participate in our discussions, and keep up with homework assignments and programming projects. The date of the midterm exam is Tuesday, February 28, 2017, the date of the final exam is TBA. The final exam will focus on material discussed in the second part of the course, but may test earlier material. You are allowed to use one double-sided page of notes in each exam.
Grading Policy: Your final grade will be determined as follows:
|Programming Assignments (CS440: P1-3, CS640: P1, P3)||25%||15%|
|Written Problem Sets||20%||10%|
|Project and presentation||0%||15%|
You are encouraged to collaborate on the solution of the homework. If you do, you must acknowledge your collaborators. Each student must submit his or her own electronic version of the solutions. If you use algorithms or code that are not your own original work and that were not provided in class or discussed in the reading materials, you must give a detailed acknowledgment of your source.
Cheating and plagiarism are not worthy of Boston University students. I expect you to abide by the rule stated above and the standards of academic honesty and computer ethics policy described in http://www.bu.edu/computing/ethics/ and http://www.bu.edu/academics/policies/academic-conduct-code.
Artificial Intelligence is an elective course that will introduce you to an exciting topic in computer science. It should be fun and not too much of a struggle for you. Make sure that you have had the prerequisites. Depending on your level of programming experience and/or mathematics background, the course may be challenging for you. If you do not understand the material, ask for help immediately. Ask questions in class. If one student is confused about something, then maybe others are also confused and grateful that someone asked. Please come and see me or Ajjen for help or send us email. Our task is to help you learn a very interesting topic.
|Dates||Topics||Readings|| Homework & Reviews
(due in class)
|2017 Th 1/19, Tu 1/24||Introduction - What is AI? Turing Test. Industry Successes, Speech Recognition. An interfaces for people with disabilities: Camera Mouse||Turing test. Russell: Intro, Intelligent Agents, Speech Recognition,||CS640: Talk reviews 1, 2, and 3 assigned.||.|
|2017 Th 1/26, Tu 1/31, Th 2/2||
Logic and Planning,
ROC analysis, confusion matrices.
Last day to ADD class: Wednesday, 2/1/17
Wikipedia on Expert Systems:
Russell: First-Order Logic, Inference. Handout on "Logic and Resolution Proofs,"
Wikipedia: 1, 2, 3.
|H1 (Speech/ExpSys/Planning) assigned H1 Solutions||.|
|2017 Tu 2/7||Resolution Proofs. Situation Variables. Planning and Acting in the Real World. Multiagent Planning.||Handouts on "Putting axioms into clause form," and "Situation Variables." Russell: Classical Planning, Planning and Acting in the Real World (Ch 8, 9, 10.1, 10.4, 11.4).||.||.|
|2017 Thu 2/9, Tu 2/14, Th 2/16||Machine Learning, Neural Nets: Backpropagation||
Learning by Training Neural Nets.
Learning from Examples. Wikipedia 1, 2, 33>
| 2/9: H1 due
2/14: CS640: Talk review 1 due
|P1 (NN) , P1 sample solution , P1 grades out|
|2017 Tu 2/21||No class. Monday schedule.||.||.||.|
|2017 Th 2/23|| Applications of Neural Nets,
Convolutional Nets, Deep Learning
Last Day to DROP Classes (without a 'W' grade): Th 2/23/17
Interview with Yann LeCun, Director of AI Research at Facebook,
|.||Fri, 2/24: P1 due.|
|2017 Tue 2/28||Midterm Exam||.||.||.|
|2017 Th 3/2||Neural Nets and Computer Vision||
|2017 3/4-3/12||Spring Recess||.||.||.|
|2017 Tu 3/14, Th 3/16||Deep Learning and Computer Vision||Lecture slides||CS640: Talk review 2 due||P2 (CV-GestureRecognition), only CS440, out|
|2017 Tu 3/21, Th 3/24||Smart Rooms, The Kids Room. Computer Vision for interactive graphics (image moments for object area, position & orientation, orientation histograms, optical flow, template matching, SSD, NCC, recursive labeling algorithm). Markov Models, Hidden Markov Models with Discrete Observations||Russell: Perception, Kids Room Lecture Slides, Bobick Freeman, Shapiro & Stockman, page 3, Rabiner (up to p. 275)||.||.|
|2017 Tu 3/28, Th 3/30||HMMs with Continuous Output Densities, Applications of HMMs: American Sign Language Recognition, Hand Tracking||Handout: HMMs with Continuous Output, Vogler (Oliver)||
3/30: CS640: Talk review 3 due
|2016 Tu 4/4|| Natural Language Processing.
Last Day to DROP Classes (with a 'W' grade) : Fri, 3/31/2017
AI with ChineseCharacteristics
||.||Mon 4/3: P2 (CV/HMM) due (only CS440)|
|2017 Th 4/6||Search techniques: Local techniques (gradient ascent, simulated annealing, genetic algorithms) and path-path techniques: Greedy search, A* Search.||Handout: Gradient Descent, Genetic Algorithm, Russell: Searching (Ch 3). A*||4/6: H2 due||4/7: CS640 Project due: gsubmit & upload to DS Bowl|
|2017 Tu 4/11, Th 4/13||Game Playing: Minimax||Russell: Adversarial Search (Ch 5), Also: Wikipedia: Minimax||.||
CS640: Final project
P3 (Game) out
|2017 Tu 4/18||CS 640 Course Project Presentations||.||.||.|
|2017 Th 4/20||Alpha-Beta, Iterative Deepening Heuristic Pruning of Game Trees.||Russell: Ch. 5.5-5.7, Handout on Static Evaluator and Alpha Beta Pruning||.||.|
|2017 Tu 4/25||Alpha-Beta Pruning, Stochastic Games. Partially Observable Games. Robotics, Humanoid personal-use robot "Pepper," Mars exploration.||
Making the Jump into Games.
Global Games Market 2016,
2016 Video Game Statistics adn Trends Who's Playing What & Why,
Russell: Robotics (Ch 25),
|H3 (CHHM, NLP, Search, Robotics) assigned||Mon 4/24: P3 (Game) due|
|2017 Th 4/27||Robot Path Planning.||Handout: Robot Path Planning, Handout: Robot's Configuration Space, Robot Motion Planning, Visibility Graph.||.||.|
|2017 Tu 5/2|| Robotics
Annoucing Winner of Game Competition: 1: Team Sean, 2: ReallyGoodTeam, 3: OK, 3: Rice & Beans
|Lecture slides. Article on Pepper,||H3 due, H3 Solutions||.|
|2017 5/9 12:30-2:30||Final Exam: Neural Net training, Computer Vision, Hidden Markov Models, NLP, Search, Games, Robotics.||.||.||.|
The lecture schedule may change depending on the time spent on each topic and whether alternative subjects are discussed. Suggestions for additional topics are welcome!
Programming Assignment Webpage Template
Graded written homeworks and solutions will be handed out in class.
Related Papers and Web Sites
Check out http://www.cs.bu.edu/faculty/betke/links.html if you need ideas for your class project, if you are looking for a job, or if you are interested in research related to AI and computer vision. You will find a list of links to conferences, journals, research groups, and companies.