Class Instructors: Przemysław ¦liwiński Witold Paluszyński | Lectures | Literature | Examination | Lab class | Lab schedule | Project class | Results
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Artificial Intelligence and Computer Vision - 2018/2019

Electronic and Computer Engineering
Faculty of Electronics



Lecture topics and notes

The following table contains the titles of all the lectures, and links to the lecture notes in PDF format. Lecture notes are provided for the convenience of the students, so it is not necessary to take notes in class. Please note that they are no substitute for textbooks, and other study materials. Further links to other literature are provided in the Literature section.

These notes are under copyright. They can be used only for anybody's private purposes, and cannot be distributed or published, for example by copying and making available from other Web pages, or in any other way.

nolectureslides
1 Introduction. Artificial intelligence. PDF
Introduction. Computer Vision.
2 Searching for solutions. Uninformed search strategies. PDF
3 Image sampling. Theory and hardware implementations.
4 Informed search strategies. Heuristic evaluations functions. PDF
5 Splines and interpolation. Demosaicking.
6 Local search algorithms. On-line search agents. Searching in games. Minimax algorithm. PDF
7 Orthogonal series and approximation.
8 Knowledge-based agents. Knowledge representation using propositional calculus and predicate calculus. PDF
9 Noise in imaging. Anscombe transform.
10 Logic programming. Horn clauses. Prolog PDF
11 Convolution and image filtering.
12 Probabilistic representation. Conditional probability. Bayes' rule. PDF
13 Shape detection, object tracking.
14 Representing knowledge in uncertain domains. Bayesian networks. PDF
15 Scene reconstruction, motion capture and gesture recognition.


Literature

Textbooks:
  1. S.J.Russell, P.Norvig, Artificial Intelligence A Modern Approach (Third Edition), Prentice-Hall, 2010, WWW
  2. Forsyth, Ponce, Computer Vision A Modern Approach, Second Edition, Prentice-Hall, 2011
  3. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011
  4. Prince, Computer Vision: Models, Learning, and Inference, Cambridge 2012
Internet resources:
  1. Artificial intelligence courses with similar programs:


Final exam

Passing and obtaining credit in this course requires successfully completing the project class for the course, and successfully passing a written examination at the end of the semester. The date of the written examination will be determined about half way through the semester, at which time the list of topics will also be announced, and example problems presented and discussed. The final course grade is computed from the exam score (weight 0.4), lab class score (weight 0.3), and the project class score (weight 0.3), rounded to the closest nominal grade.

The time and location of the written examination will be announced towards the end of the semester.

“Grandson” tests

There will be short, single question tests at all lectures. The rules:
  1. Tests are written, 3-minutes long, and can occur at any time during the lecture.
  2. The scope of the test will be the current lecture, with a possible overlap of the preceding lecture.
  3. This is a closed-book test. No books, notes, or computers can be used.
  4. Calculators are permitted. During some tests, simple numerical calculations will be necessary. A cell phone, a calculator watch, or a PDA, will be allowed. But hand calculations is also possible.
  5. Tests are graded from 1 to 3 points, with 1 point given for a blank page turned in.
  6. No-show means 0 points. No re-taking and no excuses.
  7. The final “grandson” test score will be computed by dropping the single highest score and the single lowest earned score. 0 points result for absence is never dropped.

The Android app

Android smartphone owners may find this simple application useful when writing the “grandson” tests in class: Group number calculation app
If you do not have an Android smart phone, don't worry. You may be able to use a colleague's phone, or you can do without it.

Final exam waiver

A minimum of 60% result from the “grandson” tests grants a waiver from the final exam with the grade of 4.0. Results below 60% from the “grandson” test give nothing.

The grades from the grandson test results will be counted towards the final exam as follows:

points[%] 60.0073.3386.66
grade 4.0 4.5 5.0


The Lab class

The lab class will consist of a series of assignments. A necessary and sufficient condition to obtain a passing grade for the lab class is to successfully and timely complete (obtain a positive point credit) in all, but at most one, of the assignments. Missing one assignment results only in forfeiting the credit for that assignment. Missing more than one assignment results in failing the lab class.

The schedule of the lab assignments is given below. Each assignment will be explained and discussed in class at the time of its start.

The lab assignments must be worked out individually. It is not allowed to share solutions with colleagues, or submitting results which are not the author's own. It is allowed to use any published resources, as long as they are properly credited and referenced in the report.

A written PDF report must be prepared for each assignment and turned in through the Moodle system. The detailed requirements for each report are given in the Moodle system, along with the grading criteria.

Grading the lab class

points(%) 50.0060.0070.0080.0090.00
grade 3.0 3.5 4.0 4.5 5.0

The lab assignment schedule

subjectdescriptiontools
1.Introduction to problem solving by searching clk AI Space


The Project class

The project class will consist of a two project assignments, one related to Artificial Intelligence, and one related to Computer Vision. The subject of each can be anything, but must be approved by the instructor. Both project must be completed in order to pass the project class.

Separate reports must be prepared for both assignments. Each report has to be a single-page HTML document. It must be possible to print the report. It can contain images, or even multimedia content, like applets (it is OK if the report does not print out completely). All the reports will be published by the instructor on the course Web page, so they have to be prepared accordingly. In particular, the following requirements are ESSENTIAL:

  1. the report must have a heading stating: the title of the project, the name of the author, the date, and a declaration that the report describes a project done to fulfill the requirements for this course,
  2. the report must conform to some standard of HTML, ie. must successfully verify against the standard given in the DOCTYPE declaration,
  3. the report must be spell-checked before submitting
  4. the report must contain a list of all source materials, and programming tools used,
  5. the report MUST NOT contain links allowing to download source codes from the report home server.

The report should describe the goal of the project, the methods used, the results achieved (well edited examples very welcome), the evaluation of these results, and some general conclusions or remarks. The quality of the report will be a significant factor in grading the project.

After preparing the report it has to be installed as the student's Web page on the departmental server.


“Grandson” test results

Use the following form to find your “grandson” test results. The spelling of first and last names are exactly as entered in the Edukacja system. Non-Latin letters (with accents, etc.) need to be spelled exactly as in Edukacja. (However, for the project class results, use the spelling of your name from the eportal system.) Additionally, multiple first and/or last names must be joined here with underscores, like Manuel_Antonio, or de_la_Vega.
First name: Last name:
Student number:


Class Instructors: Przemysław ¦liwiński Witold Paluszyński | Lectures | Literature | Examination | Lab class | Lab schedule | Project class | Results
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