{"id":256,"date":"2025-01-17T23:20:33","date_gmt":"2025-01-17T22:20:33","guid":{"rendered":"https:\/\/home.agh.edu.pl\/~kkulak\/?page_id=256"},"modified":"2025-01-18T16:01:07","modified_gmt":"2025-01-18T15:01:07","slug":"artificial-intelligence","status":"publish","type":"page","link":"https:\/\/home.agh.edu.pl\/~kkulak\/artificial-intelligence\/","title":{"rendered":"Artificial Intelligence"},"content":{"rendered":"\n

Both, the lecture and the lecture materials are in English. The core of the course is based on the book AIMA<\/a> Artificial Intelligence: A Modern Approach 3th edition by Russel & Norvig. During the laboratories we are studying the Clojure<\/a> – a modern JVM based dialect of Lisp.<\/p>\n\n\n\n

Literature<\/h2>\n\n\n
<\/a><\/form>

2020<\/h3>

Russel, Stuart; Norvig, Peter<\/p>

Artificial intelligence: A Modern Approach Book<\/span> <\/p>

4th, <\/span>Pearson Education Inc., <\/span>2020<\/span>, ISBN: 978-0-13-461099-3<\/span>.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@book{Russel2020aima,
\r\ntitle = {Artificial intelligence: A Modern Approach},
\r\nauthor = {Stuart Russel and Peter Norvig},
\r\nisbn = {978-0-13-461099-3},
\r\nyear  = {2020},
\r\ndate = {2020-01-01},
\r\npublisher = {Pearson Education Inc.},
\r\nedition = {4th},
\r\nabstract = {Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times , the course on artificial intelligence is ``one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.'' One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle\u2122, NOOK\u2122, and the iPhone\u00ae\/iPad\u00ae. To learn more about the course on artificial intelligence, visit http:\/\/www.ai-class.com. To read the full New York Times article, click here.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {book}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times , the course on artificial intelligence is ``one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.'' One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle\u2122, NOOK\u2122, and the iPhone\u00ae\/iPad\u00ae. To learn more about the course on artificial intelligence, visit http:\/\/www.ai-class.com. To read the full New York Times article, click here.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
2011<\/h3>
 Barber\u00e0, S.;  Beuchot, M.;  Bonet, E.;  Bonner, A.;  Colomer, J. M.;  Crossley, A. J. Fidora;  Sales, T.;  Sierra, C.;  Wyllie, G.<\/p>
Ramon Llull: From the Ars Magna to Artificial Intelligence Book<\/span> <\/p>
Artificial Intelligence Research Institute, IIIA, <\/span>2011<\/span>.<\/p>
BibTeX<\/a><\/span><\/p>
@book{Fiodora2011rlfa,
\r\ntitle = {Ramon Llull: From the Ars Magna to Artificial Intelligence},
\r\nauthor = {S. Barber\u00e0 and M. Beuchot and E. Bonet and A. Bonner and J. M. Colomer and A. J. Fidora Crossley and T. Sales and C. Sierra and G. Wyllie},
\r\neditor = {A. Fidora and C. Sierra},
\r\nyear  = {2011},
\r\ndate = {2011-01-01},
\r\npublisher = {Artificial Intelligence Research Institute, IIIA},
\r\ninstitution = {Artificial Intelligence Research Institute, IIIA},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {book}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div><\/div><\/div>
1995<\/h3>
 Graham, P<\/p>
ANSI Common Lisp Book<\/span> <\/p>
Prentice Hall, <\/span>1995<\/span>.<\/p>
BibTeX<\/a><\/span><\/p>
@book{Graham1995acl,
\r\ntitle = {ANSI Common Lisp},
\r\nauthor = {P Graham},
\r\nyear  = {1995},
\r\ndate = {1995-01-01},
\r\npublisher = {Prentice Hall},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {book}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div><\/div><\/div><\/div><\/div>\n\n\n\n
Lectures plan<\/h2>\n\n\n\n
    \n
  1. Introduction\n
      \n
    1. What is AI \u2013 intro.<\/li>\n\n\n\n
    2. Introduction to Clojure programming,<\/li>\n\n\n\n
    3. Agents,<\/li>\n\n\n\n
    4. Search,<\/li>\n\n\n\n
    5. Logic,<\/li>\n\n\n\n
    6. Planning,<\/li>\n\n\n\n
    7. Uncertainty,<\/li>\n\n\n\n
    8. Learning,<\/li>\n\n\n\n
    9. AI Robotics<\/li>\n<\/ol>\n<\/li>\n\n\n\n
    10. Agents\n
        \n
      1. Agents and environments<\/li>\n\n\n\n
      2. Rationality<\/li>\n\n\n\n
      3. PEAS (Performance measure, Environment, Actuators, Sensors)<\/li>\n\n\n\n
      4. Environment types<\/li>\n\n\n\n
      5. Agent types <\/li>\n<\/ol>\n<\/li>\n\n\n\n
      6. Solving by Searching\n
          \n
        1. Problem-solving agents<\/li>\n\n\n\n
        2. Problem types<\/li>\n\n\n\n
        3. Problem formulation<\/li>\n\n\n\n
        4. Example problems<\/li>\n\n\n\n
        5. Basic search algorithms<\/li>\n<\/ol>\n<\/li>\n\n\n\n
        6. Uncertainty\n
            \n
          1. Uncertainty<\/li>\n\n\n\n
          2. Probability<\/li>\n\n\n\n
          3. Syntax and Semantics<\/li>\n\n\n\n
          4. Inference<\/li>\n\n\n\n
          5. Independence and Bayes Rule<\/li>\n\n\n\n
          6. Bayes Rule creation<\/li>\n\n\n\n
          7. Efficient representation<\/li>\n<\/ol>\n<\/li>\n\n\n\n
          8. Probabilistic inferencing in Bayesian Networks\n
              \n
            1. Exact inference by enumeration<\/li>\n\n\n\n
            2. Exact inference by variable elimination<\/li>\n\n\n\n
            3. Approximate inference by stochastic simulation<\/li>\n\n\n\n
            4. Approximate inference by Markov chain Monte Carlo<\/li>\n<\/ol>\n<\/li>\n\n\n\n
            5. Machine Learning\n
                \n
              1. Classification vs. Regression<\/li>\n\n\n\n
              2. Spam filtering<\/li>\n\n\n\n
              3. Linear Regression<\/li>\n\n\n\n
              4. Gradient methods<\/li>\n\n\n\n
              5. Perceptron approach & linear separation<\/li>\n\n\n\n
              6. k nearest Neighbors<\/li>\n<\/ol>\n<\/li>\n\n\n\n
              7. Unsupervised Learning\n
                  \n
                1. Introduction<\/li>\n\n\n\n
                2. k-Mean<\/li>\n\n\n\n
                3. Expectation maximization (as k-Mean generalization)<\/li>\n<\/ol>\n<\/li>\n\n\n\n
                4. Planning under uncertainty\n
                    \n
                  1. Markov Decision Process (MDP)<\/li>\n\n\n\n
                  2. looking for optimal policy functions<\/li>\n\n\n\n
                  3. terms such as reward, objective<\/li>\n\n\n\n
                  4. value iteration algorithm<\/li>\n\n\n\n
                  5. Partial Observable MDP (intro)<\/li>\n<\/ol>\n<\/li>\n\n\n\n
                  6. Reinforcement Learning\n
                      \n
                    1. MDP revisited<\/li>\n\n\n\n
                    2. Temporal Difference Learning Algorithm<\/li>\n\n\n\n
                    3. Q-Learning approach<\/li>\n<\/ol>\n<\/li>\n\n\n\n
                    4. Couple of Clojure<\/a> lectures showing the basis of the language<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
                      Both, the lecture and the lecture materials are in English. The core of the course is based on the book AIMA Artificial Intelligence: A Modern Approach 3th edition by Russel & Norvig. During the laboratories we are studying the Clojure – a modern JVM based dialect of Lisp. Literature Lectures plan<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-256","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/pages\/256","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/comments?post=256"}],"version-history":[{"count":5,"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/pages\/256\/revisions"}],"predecessor-version":[{"id":329,"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/pages\/256\/revisions\/329"}],"wp:attachment":[{"href":"https:\/\/home.agh.edu.pl\/~kkulak\/wp-json\/wp\/v2\/media?parent=256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}