Osnove veštačke inteligencije i mašinskog učenja

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• Select section General

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  General

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  • Select activity Course Overview

    Course Overview

    Welcome to the "Fundamentals of Artificial Intelligence and Machine Learning" course! This course is designed for fourth-grade students and aims to provide a comprehensive understanding of AI and machine learning technologies, their applications, and their impact on various domains.

    Learning Objectives

    By the end of this course, you will:

    • Gain insights into current AI technologies and their applications.
    • Critically evaluate the possibilities and limitations of AI from a scientific perspective.
    • Understand and apply key concepts and algorithms of machine learning.
    • Learn the basics of artificial neural networks and their applications in solving real-world problems.

• Select section 1. Veštačka inteligencija (SR)

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  1. Veštačka inteligencija (SR)

  

  Uvod

  Dobrodošli na temu veštačke inteligencije (AI)! U ovom odeljku ćemo istražiti fascinantan svet AI, koji uključuje stvaranje sistema koji mogu obavljati zadatke koji obično zahtevaju ljudsku inteligenciju. Naučićete o istoriji i razvoju AI, osnovnim konceptima i etičkim razmatranjima koja okružuju njegovu upotrebu. Takođe ćemo razgovarati o različitim vrstama AI, uključujući uske, opšte i superinteligencije, i kako AI transformiše različite aspekte našeg svakodnevnog života.

  • Select activity 1.1 Koncept veštačke inteligencije

    

    1.1 Koncept veštačke inteligencije Page

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  • Select activity 1.2 Turingov test

    

    1.2 Turingov test Page

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  • Select activity 1.3 Oblasti veštačke inteligencije

    

    1.3 Oblasti veštačke inteligencije Page

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  • Select activity 1.4 Regulacija veštačke inteligencije, pravni i etički izazovi

    

    1.4 Regulacija veštačke inteligencije, pravni i etički izazovi Page

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  • Select activity 1.5 Uska, opšta i superinteligencija

    

    1.5 Uska, opšta i superinteligencija Page

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  • Select activity Intro Exercise: Jupyter sveske za vežbu

    

    Intro Exercise: Jupyter sveske za vežbu Assignment

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  • Select activity Intro Exercise: Platforma Google Colab

    

    Intro Exercise: Platforma Google Colab Assignment

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  • Select activity Intro Exercise: Bilioteka NumPy, Matplotlib i Pandas

    

    Intro Exercise: Bilioteka NumPy, Matplotlib i Pandas Assignment

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  • Select activity Vežba 1.1: Primena veštačke inteligencije - korišćenje AI programa

    

    Vežba 1.1: Primena veštačke inteligencije - korišćenje AI programa Assignment

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  • Select activity Vežba 1.2: Primena veštačke inteligencije - simulacija Turingovog testa

    

    Vežba 1.2: Primena veštačke inteligencije - simulacija Turingovog testa Assignment

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  • Select activity Vežba 1.3: Primena veštačke inteligencije - identifikovanje sistema zasnovanih na AI

    

    Vežba 1.3: Primena veštačke inteligencije - identifikovanje sistema zasnovanih na AI Assignment

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  • Select activity Vežba 1.4: Primena veštačke inteligencije - istraživanje superinteligencije

    

    Vežba 1.4: Primena veštačke inteligencije - istraživanje superinteligencije Assignment

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• Select section 2. Mašinsko učenje (SR)

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  2. Mašinsko učenje (SR)

  

  Uvod

  Dobrodošli na temu Machine Learning (ML)! Mašinsko učenje je podskup AI koji se fokusira na razvoj algoritama koji omogućavaju računarima da uče i predviđaju na osnovu podataka. U ovom odeljku ćete steći duboko razumevanje osnovnih koncepata mašinskog učenja, različitih vrsta učenja (nadgledano, bez nadzora, polu-nadzirano i učenje pojačanja) i važnost podataka u ovom procesu. Takođe ćemo pokriti korake koji su uključeni u izgradnju i validaciju modela mašinskog učenja i kako se ovi modeli mogu primeniti za rešavanje problema u stvarnom svetu.

  • Select activity 2.1 Odnos između veštačke inteligencije i mašinskog učenja

    

    2.1 Odnos između veštačke inteligencije i mašinskog učenja Page

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  • Select activity 2.2 Programiranje na osnovu podataka

    

    2.2 Programiranje na osnovu podataka Page

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  • Select activity 2.3 Osnovni koncepti mašinskog učenja

    

    2.3 Osnovni koncepti mašinskog učenja Page

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  • Select activity 2.4 Proces mašinskog učenja

    

    2.4 Proces mašinskog učenja Page

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  • Select activity 2.5 Vrste mašinskog učenja

    

    2.5 Vrste mašinskog učenja Page

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  • Select activity 2.6 Podaci u mašinskom učenju

    

    2.6 Podaci u mašinskom učenju Page

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  • Select activity 2.7 Eksplorativna analiza skupa podataka (EDA)

    

    2.7 Eksplorativna analiza skupa podataka (EDA) Page

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  • Select activity 2.8 Kreiranje reprezentacije skupa podataka

    

    2.8 Kreiranje reprezentacije skupa podataka Page

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  • Select activity 2.9 Skupovi za treniranje, validaciju i testiranje

    

    2.9 Skupovi za treniranje, validaciju i testiranje Page

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  • Select activity Vežba 2.A: Popularni skupovi podataka MNIST

    

    Vežba 2.A: Popularni skupovi podataka MNIST Assignment

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  • Select activity Vežba 2.B: Popularni skupovi podataka – ImageNet

    

    Vežba 2.B: Popularni skupovi podataka – ImageNet Assignment

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  • Select activity Vežba 2.C: Popularni skupovi podataka – COCO

    

    Vežba 2.C: Popularni skupovi podataka – COCO Assignment

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  • Select activity Vežba 3: Eksplorativna analiza skupa podataka

    

    Vežba 3: Eksplorativna analiza skupa podataka Assignment

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  • Select activity Vežba 4: Setovi za obuku, validaciju i testiranje

    

    Vežba 4: Setovi za obuku, validaciju i testiranje Assignment

    Due: Saturday, 13 March 2027, 5:00 PM

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• Select section 3. Modeli učenja (SR)

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  3. Modeli učenja (SR)

  

  Uvod

  Dobrodošli na temu Modeli učenja! Ovaj odeljak se bavi različitim modelima i tehnikama koje se koriste u mašinskom učenju za rešavanje različitih vrsta problema. Naučićete o linearnoj regresiji, klasifikaciji, stablima odlučivanja i algoritmu k-najbližih suseda, između ostalog. Razgovaraćemo o karakteristikama, prednostima i nedostacima svakog modela, kao i o važnosti validacije modela kako bi se osigurala tačnost i pouzdanost. Kroz praktične vežbe, primenite ove modele na stvarne skupove podataka i interpretirati rezultate.

  • Select activity 3.1 Linearna regresija

    

    3.1 Linearna regresija Page

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  • Select activity 3.2 Gradijentni spust

    

    3.2 Gradijentni spust Page

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  • Select activity 3.3 Polinomijalna regresija

    

    3.3 Polinomijalna regresija Page

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  • Select activity 3.4 Višestruka linearna regresija

    

    3.4 Višestruka linearna regresija Page

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  • Select activity 3.5 Klasifikacija, vrste klasifikacije i matrica konfuzije

    

    3.5 Klasifikacija, vrste klasifikacije i matrica konfuzije Page

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  • Select activity 3.6 Logistička regresija

    

    3.6 Logistička regresija Page

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  • Select activity 3.7 Stablo odlučivanja (Decision tree)

    

    3.7 Stablo odlučivanja (Decision tree) Page

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  • Select activity 3.8 Algoritam k-najbližih suseda

    

    3.8 Algoritam k-najbližih suseda Page

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  • Select activity 3.9 Hiperparametri

    

    3.9 Hiperparametri Page

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  • Select activity 3.10 Generalizacija, potprilagođavanje i preprilagođavanje

    

    3.10 Generalizacija, potprilagođavanje i preprilagođavanje Page

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  • Select activity 3.11 Unakrsna validacija

    

    3.11 Unakrsna validacija Page

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  • Select activity 3.12 Regularizacija

    

    3.12 Regularizacija Page

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  • Select activity Vežba 5.1: Linearna regresija

    

    Vežba 5.1: Linearna regresija Assignment

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  • Select activity Vežba 5.2: Gradijentni spust

    

    Vežba 5.2: Gradijentni spust Assignment

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  • Select activity Vežba 6: Logistička regresija

    

    Vežba 6: Logistička regresija Assignment

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  • Select activity Vežba 7: Stablo odlučivanja

    

    Vežba 7: Stablo odlučivanja Assignment

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  • Select activity Vežba 8: Algoritam k-najbližih suseda

    

    Vežba 8: Algoritam k-najbližih suseda Assignment

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  • Select activity Vežba 9: Validacija

    

    Vežba 9: Validacija Assignment

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• Select section 4. Neuronske mreže (SR)

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  4. Neuronske mreže (SR)

  

  Uvod

  Dobrodošli na temu neuronskih mreža! Neuronske mreže su ključna komponenta dubokog učenja, podskup mašinskog učenja koji oponaša strukturu i funkciju ljudskog mozga. U ovom odeljku ćete naučiti o osnovnim elementima neuronskih mreža, uključujući neurone, slojeve i aktivacijske funkcije. Istražićemo različite tipove arhitektura neuronskih mreža, kao što su konvolucijske i rekurentne neuronske mreže, i njihove primene u rešavanju složenih problema. Takođe ćete steći praktično iskustvo u obuci i testiranju neuronskih mreža koristeći popularne alate i okvire.

  • Select activity 4.1 Neuronske mreže

    

    4.1 Neuronske mreže Page

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  • Select activity 4.2 Obučavanje neuronskih mreža

    

    4.2 Obučavanje neuronskih mreža Page

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  • Select activity 4.3 Konvolutivne neuronske mreže (CNN)

    

    4.3 Konvolutivne neuronske mreže (CNN) Page

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  • Select activity 4.4 Rekurentne neuronske mreže

    

    4.4 Rekurentne neuronske mreže Page

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  • Select activity 4.5 Algoritam k-sredina

    

    4.5 Algoritam k-sredina Page

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  • Select activity Vežba 10: Obučavanje neuronskih mreža

    

    Vežba 10: Obučavanje neuronskih mreža Assignment

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  • Select activity Vežba 11: Mreža VGG-16 i zadatak klasifikacije

    

    Vežba 11: Mreža VGG-16 i zadatak klasifikacije Assignment

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  • Select activity Vežba12: Algoritam K-sredina

    

    Vežba12: Algoritam K-sredina Assignment

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• Select section 5. Reference

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  5. Reference

  • Select activity GitHub repostitory

    

    GitHub repostitory URL

    https://github.com/AIForVet/aiml

  • Select activity ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN

    

    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN File DOCX

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  • Select activity ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN

    

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  • Select activity Curriculum: Fundamentals of Artificial Intelligence and Machine Learning

    

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  • Select activity https://scikit-learn.org/

    

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  • Select activity https://petlja.org/sr-Latn-RS/kurs/11203/0

    

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  • Select activity Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow -Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-Aurélien-Géron-O’Reilly-Media-2019

    

    Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow -Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-Aurélien-Géron-O’Reilly-Media-2019 File PDF

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  • Select activity machine-learning-and-artificial-intelligence-1st-ed-2020-978-3-030-26621-9-978-3-030-26622-6 compress

    

    machine-learning-and-artificial-intelligence-1st-ed-2020-978-3-030-26621-9-978-3-030-26622-6 compress File PDF

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  • Select activity Explorations in Artificial Intelligence and Machine Learning

    

    Explorations in Artificial Intelligence and Machine Learning URL

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  • Select activity Machine Learning For Absolute Beginners

    

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  • Select activity Artificial Intelligence A Modern Approach 3rd

    

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  • Select activity Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    

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  • Select activity Hands-On AI Projects for the Classroom

    

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  • Select activity Book

    

    Book

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• Select section Map of content

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  Map of content
• Select section Domaći

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  Domaći

  • Select activity ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS SR

    

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  • Select activity Lekcije - spisak

    

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  • Select activity Lekcije - Upload

    

    Lekcije - Upload Assignment

    Due: Sunday, 2 May 2027, 11:59 PM

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