Teadmispõhise tarkvaraarenduse meetodid / Methods of Knowledge Based Software Development - 2015

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Course code: ITI8600 (Ainekaart eesti keeles ITI8600)

Language: The default language of the course is English, but if all students understand Estonian, it will be in Estonian

Lecturers:

  • Tanel Tammet, tanel.tammet@ttu.ee, 6203457, TTÜ ICT-426
  • Juhan Ernits, juhan.ernits@ttu.ee, 6202326, TTÜ ICT-428 (handles ÕIS registrations)


Past editions

This course will be offered for the first time in 2015. It is the result of combining three previously offered courses, Knowledge Search, Formalization and Storing, Principles of Artificial Intelligence and applied logic

Time, place, result

  • Lectures: Mondays 17:45-19:15, SOC-211B, SOC-211C
  • Labs: Tuesdays 17:45-19:15, ICT-401
  • Exam:
    • Monday, Jan 11, 10:00-12:30, U05-103. Reg deadline in ÕIS: Jan 8, 2016.
    • Monday, Jan 18, 10:00-12:30, U05-103. Reg deadline in ÕIS: Jan 15, 2016.

Please register to the appropriate exam in ÕIS.

Grading

The final grade will be based on 40% of points from homework assignments and 60% of the result of an exam.

There will be four homework assignments, one for each block. Assignments will give up to 10 points each. In order to successfully pass the course, at least three homeworks must be successfully defended.

Homeworks can be done alone or in pairs. Pairs will be formed randomly by the lecturers, separately for each homework. As said, you can always opt to do it alone.

Homework has to be presented during lab time to the lecturer on site: email submissions are not accepted. Both pair members must be present during presentation: in case one of them is not present, the homework of the missing person is not considered to be defended. It is also not guaranteed that both pair members get the same grade.

The homeworks have to be submitted to the university git and then defended: git details will be presented later by Juhan.

Homework deadline policy:

  • Defended code must be submitted for defence latest one date before the defence deadline (example: defence deadline 22. Sept, submission 21. Sept).
  • In case the homework is defended in time, you have one extra week to add missing details/improvements without losing points.
  • In case the homework is not defended in time, you have two extra weeks to defend it, but in this case you will get only half the points.
  • No homeworks are accepted after the two extra weeks after the deadline have passed.
  • In order to be accepted to exam you have to successfully defend at least three of the four homeworks.

Grades and additional homework info available at https://ained.ttu.ee

Exam

Exam dates:

  • January 11, U05-103
  • January 18, U05-103

Q&A sessions:

  • January 8, at 14:00, ICT-411
  • January 15, at 14:00, ICT-411

The exam will contain one block of questions per each of the four blocks below. The exact requirements and materials to read are these:

Materials for search algorithms

The search algorithms block was based on the following chapters from the book Artificial Intelligence, a Modern Approach, 3rd Edition, by Stewart Russell and Peter Norvig. (The book is available in TUT library as [1] and [2]):

  • Chapter 3: Solving problems by searching
  • Chapter 4: Beyond classical search
  • Chapter 5: Adversarial search
  • Chapter 6: Constraint satisfaction problems

In particular, it will be necessary to be able to choose best methods from the ones mentioned in those chapters for solving particular problems. In addition it is necessary to be able to charachterize the properties of these approaches in terms of relevant criteria (branching factor, time complexity, space complexity, completeness).

Materials for knowledge representation

The exam question will consist of a small exercise in encoding given knowledge, not theoretical questions.

In order to do the exercise you need to:

  • Understand the relationships and be able to transform between "standard relational databases" aka SQL bases, RDF, RDFS and 1st order logic. Encoding SQL tables in RDF, encoding RDF in logic, encoding SQL queries in 1st order logic. Understand all of the first lecture notes as ppt and as as pdf.
  • Understand basics of RDF and RDFS from RDF, RDFs, beginning of w3c RDF primer. Understand basics of RDFA, see RDFa in wikipedia and beginning of w3c RDFa primer (read it while avoiding complex details). OWL and the other complex special rule languages are not required.

Materials for reasoning and deduction

Again, the exam question will consist of a small exercise in encoding given knowledge and performing derivations, not theoretical questions.

In order to do the exercise you need to:

  • Be able to do small derivations in first order logic using the basic resolution method. Using equality and special strategies not required. See for intro and use the chapters "Resolution", "The Resolution Procedure" and "The ANL Loop" in a book by Geoff to check out and understand simple derivation examples. Be able to encode RDF and RDFS rules in 1st order logic and to perform derivations, see the materials from the previous section.
  • Understand the core ideas of nonmonotonic logic (avoid complex details here, just scan to get an overview). Understand the basics of default logic (be able to encode small problems and perform derivations).
  • Understand the core ideas and principal differences between probabilistic and fuzzy logic, using this material‎ (thorough details, encoding examples, formulas and calculations not required).

Materials for learning

The learning exam questions will be based on the following materials:

  • Chapter 18: Learning from examples. In Artificial Intelligence, a Modern Approach, 3rd Edition
  • Chapter 3 (3.1, 3.2, 3.5): Linear regression. In [Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
  • Chapter 4 (4.1, 4.2, 4.3, 4.6.5) Logistic regression, comparison with K-nearest neighbours, in ISL
  • Chapter 5 (5.1, 5.2) Cross-Validation, Bootstrap. In ISL
  • Chapter 8 (8.1, 8.2) Decision trees, Bagging, Random Forests, Boosting. In ISL
  • Chapter 9 (9.1, 9.2 (9.3-5)) Support vector machines. In ISL
  • Perceptron and neural networks (based on the lecture slides)

Course structure

The course will consist of four interconnected blocks covering crucial areas of the subject:

Search algorithms

Homework is available in Moodle. To log in you will need to use your TUT e-mail account in Office 365. The groups have been assigned to participants who registered to the course in Moodle on September 14. Homework defence deadline: 22. September.

NB! Konsultatsioon / Q&A session 1: September 17, 17.00-19.00 in room ICT-411. If the room is empty, please call 2326 from the door phone of the department of CS.

Knowledge representation

Knowledge representation homework

Homework defence deadline: 3. November. Presentation afer this deadline will give half of the points. Absolute deadline 1 December.

Work should be submitted to git. Latest one day before deadline.

New groups and repositories are available.

Useful in-depth material for reading as free pdf-s:

Block structure:

Intro: SQL, logic and RDF.

First lecture as ppt and as as pdf

Natural language and restricted natural language

Useful NLP links and notes

Lecture intro as ppt and as as pdf

See also about IBM Watson

RDFa, RDFs, OWL and rules

Lecture intro as ppt and as as pdf

Context, metainformation and rules.

Reasoning and deduction

Automated reasoning homework 2015


Homework defence deadline: 1. December. No presentations accepted after 8. December.

Useful books for reading:


Test and compare simple propositional solver algorithms:

Block structure:

First order logic solvers

Propositional solvers

SMT solvers

Logic for uncertain knowledge

Interesting to try out:

Things we looked at before:

Learning

Homework task

(3 best results of 4 homeworks will be taken into account in marking.)

The task is to sign up to the "What's Cooking" challenge at Kaggle, download the data and evaluate at least 3 different learning algorithms in Scikit learn on that data. You should submit the appropriate scripts and a report of the results with explanation. NB! It is necessary to run cross-validation in all cases and explain why you think why some approach performs better than the other.

It is encouraged but not mandated to submit your best result to Kaggle.

Homework defence deadline: 15. December.

Additional reading

Introduction to Statistical Learning, a freely available book. The Elements of Statistical Learning, a freely available book including a bit more advanced treatment of topics in machine learning.

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