COMP 3211 - Spring 2016
You are welcome to knock on the door of the instructor any time. The TAs' office hours are posted at http://course.cs.ust.hk/comp3211/ta/.
Welcome to COMP3211! (This course was formerly called COMP221.) Tutorials will begin after Week 2.
Always check the Discussion Forum for up-to-the-minute
Discussion forum is at http://comp151.cse.ust.hk/~dekai/content/?q=forum/3.
Always read before asking/posting/emailing your question.
Course home page is at http://www.cs.ust.hk/~dekai/3211/.
Tutorial info is at http://course.cs.ust.hk/comp3211/ta/.
COMP 3211. Foundations underlying design of intelligent systems. Relations between logical, statistical, cognitive, biological paradigms; basic techniques for heuristic search, theorem proving, knowledge representation, adaptation; applications in vision, language, planning, expert systems.
- Identify the fundamental concepts and techniques of AI: autonomous agents, search, knowledge representation, and machine learning.
- Understand and apply techniques for searching state spaces, including breadth-first, depth-first, best-first, A* search, minmax game tree search, minmax with alpha-beta pruning, and hill-climbing search.
- Appreciate some cutting edge research in AI such as multiagent systems, game theory, ontology, semantic web, big data, deep learning, and others.
- Introduction to Text Alignment: Statistical Machine Translation Models from Bitexts to Bigrammars (forthcoming), by Dekai WU. Springer, 2016.
- Artificial Intelligence: A Modern Approach (2nd Edition), by Stuart RUSSELL and Peter NORVIG. Prentice-Hall, 2003. ISBN-13: 978-0137903955.
- Structure and Interpretation of Computer Programs (2nd edition),
by Harold ABELSON and Gerald Jay SUSSMAN,
with Julie SUSSMAN. MIT Press, 1984. ISBN-10: 0-262-01077-1.
Full text and code are available online at no cost for the Scheme book (Structure and Interpretation of Computer Programs) at http://mitpress.mit.edu/sicp/.
All materials submitted for grading must be your own work. You are advised against being involved in any form of copying (either copying other people's work or allowing others to copy yours). If you are found to be involved in an incident of plagiarism, you will receive a failing grade for the course and the incident will be reported for appropriate disciplinary actions.
Warning: sophisticated plagiarism detection systems are in operation!
CollaborationYou are encouraged to collaborate in study groups. However, you must write up solutions on your own. You must also acknowledge your collaborators in the write-up for each problem, whether or not they are classmates. Other cases will be dealt with as plagiarism.
The course will be graded on a curve, but no matter what the curve is, I guarantee you the following.
|If you achieve||85%||you will receive at least a||A||grade.|
Your grade will be determined by a combination of factors:
ExaminationsNo reading material is allowed during the examinations. No make-ups will be given unless prior approval is granted by the instructor, or you are in unfavorable medical condition with physician's documentation on the day of the examination. In addition, being absent at the final examination results in automatic failure of the course according to university regulations, unless prior approval is obtained from the department head.
There will be one midterm worth approximately 20%, and one final exam worth approximately 25%.
Science and engineering (including software engineering!) is about communication between people. Good participation in class and/or the online forum will count for approximately 5%.
All assignments must be submitted by 23:00 on the due date. Scheme programming assignments must run under Chicken Scheme on Linux. Assignments will be collected electronically using the automated CASS assignment collection system. Late assignments cannot be accepted. Sorry, in the interest of fairness, exceptions cannot be made.
Programming assignments will account for a total of approximately 50%.
All information for tutorials is at http://course.cs.ust.hk/comp3211/ta/.
|2016.02.01||1||Lecture||Does God play dice? Assumptions: scientific method, hypotheses, models, learning, probability; linguistic relativism and the Sapir-Whorf hypothesis; inductive bias, language bias, search bias; the great cycle of intelligence|
|2013.09.03||1||Lecture||Languages of the world
Admiinistrivia (honor statement, HKUST classroom conduct)
|2016.09.08||2||Holiday||Lunar New Year|
|2016.09.10||2||Holiday||Lunar New Year|
|2016.02.12||2||Lecture||Learning to translate: engineering, social, and scientific motivations; "It's all Chinese to me": linguistic complexity; challenges in modeling translation [at tutorial]|
|2015.02.15||3||Lecture||Is machine translation intelligent? Interactive simulation|
|2016.02.17||3||Lecture||Evaluating translation quality: adequacy, fluency, fidelity, speed, memory, n-grams, BLEU|
|2013.02.19||3||Lecture||Functional programming; Scheme [at tutorial]|
|2016.02.24||4||Lecture||Basic probability theory; conditional probabilities; Bayes' theorem|
|2016.02.29||5||Lecture||Introduction to search; anagrams|
|2016.03.02||5||Lecture||Markov models, n-gram models|
|2016.03.07||6||Lecture||Uninformed search; BFS, DFS, depth-bounded search, iterative deepening|
|2016.03.09||6||Lecture||Informed search; Dijkstra's shortest path algorithm|
|2016.03.14||7||Lecture||Anagrams with replacement; Chinese anagrams; word n-grams|
|2016.03.21||8||Lecture||HMM/SFSA/WFSA: hidden Markov models, finite-state models; parts of speech; generation vs recognition/parsing|
|2016.03.23||8||Lecture||Converting state-based to transition based FSAs; segmental HMM/SFA/WFSAs|
|2016.03.30||8||Lecture||HMM/SFSA/WFSA decoding, evaluation, learning: unrolling in time; ways to traverse the lattice; formalization for Viterbi decoding and evaluation [slides]|
|2016.04.04||9||Holiday||Ching Ming Festival|
|2016.04.11||10||Lecture||HMM/SFSA/WFSA: forward algorithm, backward algorithm, expectations|
|2016.04.13||10||Lecture||HMM/SFSA/WFSA: forward-backward algorithm, expectation maximization (EM) algorithm|
|2016.04.15||10||Lecture||In-class quiz/exercise: expectation maximization (EM) algorithm|
|2016.04.18||11||Lecture||In-class quiz/exercise: expectation maximization (EM) algorithm|
|2016.04.20||11||Lecture||Heuristic functions; admissibility; A* search|
|2016.04.22||11||Lecture||Knowledge representation: conjunctive normal form; 3-CNF; AND/OR graphs; FSGs (finite-state grammars) and segmental FSGs; FSGs as hypergraphs; Knuth's algorithm|
|2016.04.25||12||Lecture||FOPL (first-order predicate logic); CFGs (context-free grammars); DCGs (definite clause grammars); stochastic CFGs; weighted CFGs; segmental CFGs; instantiating CFGs in time|
|2016.04.27||12||Lecture||Applying abstract models to different task domains; HMM alignment|
|2016.05.04||13||Lecture||Representing compositional knowledge; syntax-directed transduction grammars; inversion transduction grammars [article]; generative capacity of ITGs; bracketing ITGs; optimal bilingual parsing for alignment, bibracketing, translation-driven segmentation, learning phrasal translation lexicons, projection/coercion|
|2016.05.09||13||Lecture||Knowledge representation with semantic frames; the magic number 4: how the generative capacity of ITGs explains the evolution of semantic frame structure|
|2016.05.19||14||Exam||COMP3211 Final [TKPH, 08:30-11:30]|
- Scheme slides
- Scheme R5RS [html, pdf]
- Chicken Scheme 4.10 manual
- Chicken Scheme 4 eggs
- A1 (due 2016.04.01)
Last updated: 2016.05.09