Artificial intelligence (CK0031)

The course overviews selected topics in artificial intelligence. The course deals with some of the central principles of AI, including search and problem-solving methods, reasoning and decision making under uncertainty:
  1. Agents and environment: Rationality, Nature of the environment, Structure of the agents;
  2. Problem solving: Searching discrete environments and numerical optimisation;
  3. Probabilistic reasoning: Inference in probabilistic models (and, perhaps, a mention to learning in probabilistic models.)
What the course does not deal with is Logic and Machine Learning. We offer more suitable courses to study those topics.

Instructor : Francesco Corona (FC): francesco döt corona ät ufc döt br
Teaching assistants : Julio Alberto Sibaja Rettes (JA ), jasr ät ask döt him; Jean Carllo Jardim Costa (JC ), jcjc ät ask döt him; and, Saulo Anderson Freitas de Oliveira (S ), safo ät ask döt him

Physical location : Monday, Wednesday and Friday 10:00-12:00. Bloco 915, Sala 1074.
Internet location : Here! Or, here (CK0031) for mambojumbo related to administration.

Evaluation : Approx. half a dozen theoretical and practical problem sets (EXs) will be assigned as homework. Partial evaluations (APs) will consist of exercises that are randomly drawn from the aformentioned sets. The APs must be worked out in the classroom, individually.

Grading : The grade is chosen as the highest one resulting from the two following schemes:
1/3*[0.4*AVG(EX00,EX01,EX02) + 0.60*AP01] + 1/3*[1.0*EX03] + 1/3*[0.4*AVG(EX04,EX05) + 0.6*AP02]
1/3*[0.4*AVG(EX00,EX01) + 0.60*(AP01_part1)] + 1/3*[0.4*AVG(EX02,EX03) + 0.60*(AP01_part2 U AP00)] + 1/3*[0.4*AVG(EX04,EX05) + 0.6*AP02]

The following notation holds:
- The operator AVG() denotes the average (arithmetic mean) of the grades of the objects between round brackets ();

- EX00 is the grade from this exercise set
- EX01 is the grade from this exercise set
- EX02 is the grade from this exercise set
- EX03 is the grade from this exercise set
- EX04 is the grade from this exercise set
- EX05 is the grade from this exercise set

- AP01 is the grade from this partial evaluation (or this, if you resit)
- AP01_part1 is the grade from questions I00, I01 and I02 from AP01
- AP01_part2 is the grade from questions I03 and I04 from AP01

- AP02 is the grade from this partial evaluation

- AP00 is the grade from this partial evaluation and AP01_part2.

- The letter U in (AP01_part2 U AP00) denotes the union symbol, thus (AP01_part2 U AP00) is the grade from combining questions I03 and I04 (from AP01) and all the questions from AP00

Final grades: Grades between 4 and 7 are entitled to have a final evaluation (AF, see below).

>>>>>> ! <<<<<<
AF (three questions) is Friday Dec 16 - 10:00-12:00 - Sala 1074, Bloco 915.
Please, send email to FC to confirm participation and minimise printing.
>>>>>> ! <<<<<<

Go to:   Lectures and schedule | Problem sets | Supplementary material | As it pops out |

Lectures and schedule

We meet on Wednesday AUG 17 at 10:15am (give or take 5), to briefly introduce each other and discuss some practicalities.
  1. About this course

    A About this course (FC)
    • Slides ( AUG 29, AUG 31)
    • Exercises ( SEP 02) Hand-in by SEP 11 at 23:59:59 Fortaleza time
    • Results in [0,10] (Scores are PRELIMINARY: Submissions will be subjected to a 2nd round of evaluation)
    • About the type of artificial intelligence that we shall study and the type that we shall not study in this course

  2. Agents and environment

    A Agents and environments (FC)
    • Slides ( SEP 05, SEP 09)
    • Exercises ( SEP 12, SEP 14 and SEP 26, with S and JA in LEC I) Hand-in by OCT 09 at 23:59:59 Fortaleza time
    • Results in [0,1] (Scores are PRELIMINARY: Submissions will be subjected to a 2nd round of evaluation)
    • Structure of agents, nature of environments

  3. Problem solving

  4. Probabilistic reasoning


Problem sets

As we use problem set questions covered by books, papers and webpages, we expect you not to copy, refer to, or look at the solutions in preparing your answers. We expect you to want to learn and not google for answers: If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution.

The purpose of problem sets is to help you think about the material, not just give us the right answers.

Homeworks must be done individually: Each of you must hand in his/her own answers. In addition, each of you must write his/her own code when requested. It is acceptable, however, for you to collaborate in figuring out answers. We are assuming that you take the responsibility to make sure you personally understand the solution to any work arising from collaboration (though, you must indicate on each homework with whom you collaborated).

To typeset assignments, students and teaching assistants are encouraged to use a common LaTeX template: Source (PDF).

Assignments must be returned via SIGAA - Delays will be penalised (<24h: 20% penalty; <48h: 40% penalty; ...).



Course slides will suffice. Slides are mostly based on the three following textbooks: The material can be complemented using material from the following textbooks (list not exhaustive):
  1. Bayesian networks and decision graphs, by Finn Jensen and Thomas Nielsen
  2. Probabilistic graphical models: principles and techniques, by Daphne Koller and Nir Friedman
  3. Probabilistic reasoning in intelligent networks: Networks of plausible inference, by Judea Pearl
  4. Practical methods of optimization, by R. Fletcher
  5. Convex optimization (Book website), by Stephen Boyd and Lieven Vandenberghe
  6. Heuristic Search: Theory and Applications, by Stefan Edelkamp and Stefan Schrödl
Copies of these books are floating around.

>>>>>> Course material is prone to a typo or two - Please inbox FC to report <<<<<<


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