Artificial Intelligence and Machine Learning Fundamentals

Section outline

• Select section General

  Collapse Expand

  General

  Collapse all Expand all

  • 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. Artificial Intelligence (EN)

  Collapse Expand

  1. Artificial Intelligence (EN)

  

  Introduction

  Welcome to the topic of Artificial Intelligence (AI)! In this section, we will explore the fascinating world of AI, which involves creating systems that can perform tasks typically requiring human intelligence. You will learn about the history and development of AI, the fundamental concepts, and the ethical considerations surrounding its use. We will also discuss the different types of AI, including narrow, general, and superintelligence, and how AI is transforming various aspects of our daily lives.

  • Select activity 1.1 The Concept of Artificial Intelligence

    

    1.1 The Concept of Artificial Intelligence Page

    Completion

    Students must

    

    View

    

  • Select activity 1.2 Turing's test

    

    1.2 Turing's test Page

    Completion

    Students must

    

    View
  • Select activity 1.3 Areas of Artificial Intelligence

    

    1.3 Areas of Artificial Intelligence Page

    Completion

    Students must

    

    View
  • Select activity 1.4 Regulation of Artificial Intelligence, Legal and Ethical Challenges

    

    1.4 Regulation of Artificial Intelligence, Legal and Ethical Challenges Page

    Completion

    Students must

    

    View
  • Select activity 1.5 Narrow, General, and Superintelligence

    

    1.5 Narrow, General, and Superintelligence Page

    Completion

    Students must

    

    View
  • Select activity Intro Exercise: Jupyter Exercise Notebooks

    

    Intro Exercise: Jupyter Exercise Notebooks Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Intro Exercise: Google Colab platform

    

    Intro Exercise: Google Colab platform Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Intro Exercise: NumPy, Matplotlib and Pandas libraries

    

    Intro Exercise: NumPy, Matplotlib and Pandas libraries Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 1.1: Application of Artificial Intelligence - Using AI Programs

    

    Exercise 1.1: Application of Artificial Intelligence - Using AI Programs Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 1.2: Application of Artificial Intelligence - Simulating the Turing Test

    

    Exercise 1.2: Application of Artificial Intelligence - Simulating the Turing Test Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 1.3: Application of Artificial Intelligence - Identifying AI-Based Systems

    

    Exercise 1.3: Application of Artificial Intelligence - Identifying AI-Based Systems Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 1.4: Application of Artificial Intelligence - Exploring Superintelligence

    

    Exercise 1.4: Application of Artificial Intelligence - Exploring Superintelligence Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Title: Artificial Intelligence. outcomes and content

    Title:   Artificial Intelligence. (8T+4E)
    OUTCOMES Upon completion of the topic, the student will be able to:	RECOMMENDED CONTENT AND KEY CONTENT CONCEPTS
    – Explain the concept of artificial intelligence. – describe the Turing's test; – recognize the systems in their environment that are based on artificial intelligence and their possibilities and potentials in everyday life; – explain the practical and ethical problems of the use of artificial intelligence; – assess the advantages, disadvantages, challenges and consequences of the use of artificial intelligence; – distinguish between general, narrow and superintelligence; – illustrates the possibility of applying artificial intelligence on examples from everyday life; – It uses existing artificial intelligence systems to respond to the request.	– The concept of artificial intelligence. – Turing's test. – The field of artificial intelligence... – Regulation of Artificial Intelligence, Legal and Ethical Challenges – Narrow, General and Superintelligence. Exercises: 1. Application of artificial intelligence.  Key terms:   artificial intelligence, Turing test.

• Select section 2. Machine Learning (EN)

  Collapse Expand

  2. Machine Learning (EN)

  

  Introduction

  Welcome to the topic of Machine Learning (ML)! Machine learning is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions based on data. In this section, you will gain a deep understanding of the basic concepts of machine learning, the different types of learning (supervised, unsupervised, semi-supervised, and reinforcement learning), and the importance of data in this process. We will also cover the steps involved in building and validating machine learning models, and how these models can be applied to solve real-world problems.

  • Select activity 2.1 The Relationship between Artificial Intelligence and Machine Learning

    

    2.1 The Relationship between Artificial Intelligence and Machine Learning Page

    Completion

    Students must

    

    View
  • Select activity 2.2 Data-driven programming

    

    2.2 Data-driven programming Page

    Completion

    Students must

    

    View
  • Select activity 2.3 Basic concepts of machine learning

    

    2.3 Basic concepts of machine learning Page

    Completion

    Students must

    

    View
  • Select activity 2.4 A machine learning process

    

    2.4 A machine learning process Page

    Completion

    Students must

    

    View
  • Select activity 2.5 Types of machine learning

    

    2.5 Types of machine learning Page

    Completion

    Students must

    

    View
  • Select activity 2.6 Data in Machine Learning

    

    2.6 Data in Machine Learning Page

    Completion

    Students must

    

    View
  • Select activity 2.7 Exploratory Data Analysis (EDA)

    

    2.7 Exploratory Data Analysis (EDA) Page

    Completion

    Students must

    

    View
  • Select activity 2.8 Creating a Representation of a Dataset

    

    2.8 Creating a Representation of a Dataset Page

    Completion

    Students must

    

    View
  • Select activity 2.9 Training, validation and testing sets

    

    2.9 Training, validation and testing sets Page

    Completion

    Students must

    

    View
  • Select activity Exercise 2.A: Popular datasets MNIST

    

    Exercise 2.A: Popular datasets MNIST Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 2.B: Popular datasets - ImageNet

    

    Exercise 2.B: Popular datasets - ImageNet Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 2.C: Popular datasets - COCO

    

    Exercise 2.C: Popular datasets - COCO Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 3: Exploratory analysis of a data set

    

    Exercise 3: Exploratory analysis of a data set Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 4: Training, validation and testing sets

    

    Exercise 4: Training, validation and testing sets Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Machine Learning (outcomes and content)

    TOPIC TITLE:   Machine Learning (12T+6E)
    OUTCOMES Upon completion of the topic, the student will be able to:	RECOMMENDED CONTENT AND KEY CONTENT CONCEPTS
    – Explain the concept of machine learning and its place in relation to artificial intelligence. – Explain the basic concepts of machine learning. – Illustrates how machine learning troubleshooting differs from traditional approaches. – Explain the difference between the types of machine learning. – Propose a machine learning method and explain the data collection procedure for the problem it solves. – explain how data sets are formed; – recognizes real-world problems as amenable to machine learning; – prepares data that feeds into the model in a way that suits the problem it is trying to solve.	– The Relationship between Artificial Intelligence and Machine Learning. – Data-driven programming – Basic concepts of machine learning. – A machine learning process. – A type of machine learning. – Data in Machine Learning; Examples of popular and publicly available datasets. – Exploratory Data Analysis (EDA). – Training, validation and testing sets. Exercises: 1. Popular datasets. 2. Exploratory analysis of a data set. 3. Training, validation and testing sets.  Key terms:   machine learning, exploratory analysis of a dataset, training set, test suite, validation suite.

• Select section 3. Training Models (EN)

  Collapse Expand

  3. Training Models (EN)

  

  Introduction

  Welcome to the topic of Learning Models! This section delves into the various models and techniques used in machine learning to solve different types of problems. You will learn about linear regression, classification, decision trees, and the k-nearest neighbors algorithm, among others. We will discuss the characteristics, advantages, and disadvantages of each model, and the importance of model validation to ensure accuracy and reliability. Through practical exercises, you will apply these models to real datasets and interpret the results.

  • Select activity 3.1 Linear Regression

    

    3.1 Linear Regression Page

    Completion

    Students must

    

    View
  • Select activity 3.2 Gradient Descent

    

    3.2 Gradient Descent Page

    Completion

    Students must

    

    View
  • Select activity 3.3 Polynomial regression

    

    3.3 Polynomial regression Page

    Completion

    Students must

    

    View
  • Select activity 3.4 Multiple linear regression

    

    3.4 Multiple linear regression Page

    Completion

    Students must

    

    View
  • Select activity 3.5 Classification, types of classification, and matrix of confusion

    

    3.5 Classification, types of classification, and matrix of confusion Page

    Completion

    Students must

    

    View
  • Select activity 3.6 Logistic regression

    

    3.6 Logistic regression Page

    Completion

    Students must

    

    View
  • Select activity 3.7 Decision tree

    

    3.7 Decision tree Page

    Completion

    Students must

    

    View
  • Select activity 3.8 K-Nearest Neighbor (kNN) Algorithm

    

    3.8 K-Nearest Neighbor (kNN) Algorithm Page

    Completion

    Students must

    

    View
  • Select activity 3.9 Hyperparameters

    

    3.9 Hyperparameters Page

    Completion

    Students must

    

    View
  • Select activity 3.10 Generalization, under-adaptation, and over-adaptation

    

    3.10 Generalization, under-adaptation, and over-adaptation Page

    Completion

    Students must

    

    View
  • Select activity 3.11 Validation, cross-validation

    

    3.11 Validation, cross-validation Page

    Completion

    Students must

    

    View
  • Select activity 3.12 Regularization

    

    3.12 Regularization Page

    Completion

    Students must

    

    View
  • Select activity Exercise 5.1: Linear regression

    

    Exercise 5.1: Linear regression Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 5.2: Gradient Descent

    

    Exercise 5.2: Gradient Descent Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 6: Logistic Regression

    

    Exercise 6: Logistic Regression Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 7: Decision tree

    

    Exercise 7: Decision tree Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 8: The k-nearest neighbor algorithm

    

    Exercise 8: The k-nearest neighbor algorithm Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 9: Validation

    

    Exercise 9: Validation Assignment

    Completion

    Students must

    

    Make a submission
• Select section 4. Neural Networks (EN)

  Collapse Expand

  4. Neural Networks (EN)

  

  Introduction

  Welcome to the topic of Neural Networks! Neural networks are a key component of deep learning, a subset of machine learning that mimics the structure and function of the human brain. In this section, you will learn about the basic elements of neural networks, including neurons, layers, and activation functions. We will explore different types of neural network architectures, such as convolutional and recurrent neural networks, and their applications in solving complex problems. You will also gain hands-on experience in training and testing neural networks using popular tools and frameworks.

  • Select activity 4.1 Neural networks

    

    4.1 Neural networks Page

    Completion

    Students must

    

    View
  • Select activity 4.2 Training of neural networks

    

    4.2 Training of neural networks Page

    Completion

    Students must

    

    View
  • Select activity 4.3 Convolutional Neural Networks (CNN)

    

    4.3 Convolutional Neural Networks (CNN) Page

    Completion

    Students must

    

    View
  • Select activity 4.4 Recurrent neural networks

    

    4.4 Recurrent neural networks Page

    Completion

    Students must

    

    View
  • Select activity 4.5 K-Means algorithm

    

    4.5 K-Means algorithm Page

    Completion

    Students must

    

    View
  • Select activity Exercise 10: Training neural networks

    

    Exercise 10: Training neural networks Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 11: VGG-16 Network and Image Classification Task

    

    Exercise 11: VGG-16 Network and Image Classification Task Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Exercise 12: K-Means algorithm

    

    Exercise 12: K-Means algorithm Assignment

    Completion

    Students must

    

    Make a submission
  • Select activity Neural Networks (outcomes and content)

    Completion

    Students must

    

    Mark as done

    TOPIC TITLE:   Neural Networks (8T+4E)
    OUTCOMES Upon completion of the topic, the student will be able to:	RECOMMENDED CONTENT AND KEY CONTENT CONCEPTS
    – explain the context of neural networks and deep learning; – list typical cases when neural networks can be applied to solve problems; – describe the process of training a neural network; – List the basic properties and types of architectures of artificial neural networks; – Apply a procedure to solve a given problem using neural networks.	– Neural networks. – Training of neural networks. – Types of neural networks. Exercises: 1 . Training neural networks.  Key concepts:   Artificial neural networks.

• Select section 5. Reference

  Collapse Expand

  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

    Completion

    Students must

    

    View
  • Select activity ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN

    

    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN File PDF

    Completion

    Students must

    

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

    

    Curriculum: Fundamentals of Artificial Intelligence and Machine Learning Folder

    Completion

    Students must

    

    Mark as done
  • Select activity https://scikit-learn.org/

    

    https://scikit-learn.org/ URL

    Completion

    Students must

    

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

    

    https://petlja.org/sr-Latn-RS/kurs/11203/0 URL

    Completion

    Students must

    

    View
  • 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

    Completion

    Students must

    

    Mark as done
  • 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

    Completion

    Students must

    

    Mark as done
  • Select activity Explorations in Artificial Intelligence and Machine Learning

    

    Explorations in Artificial Intelligence and Machine Learning URL

    Completion

    Students must

    

    Mark as done
  • Select activity Machine Learning For Absolute Beginners

    

    Machine Learning For Absolute Beginners File PDF

    Completion

    Students must

    

    Mark as done
  • Select activity Artificial Intelligence A Modern Approach 3rd

    

    Artificial Intelligence A Modern Approach 3rd File PDF

    Completion

    Students must

    

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

    

    Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville URL

    Completion

    Students must

    

    Mark as done
  • Select activity Hands-On AI Projects for the Classroom

    

    Hands-On AI Projects for the Classroom File PDF

    Completion

    Students must

    

    Mark as done
  • Select activity Book

    

    Book

    Completion

    Students must

    

    View
• Select section Map of content

  Collapse Expand

  Map of content
• Select section Domaći

  Collapse Expand

  Domaći

  • Select activity ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS SR

    

    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS SR File DOCX

    Completion

    Students must

    

    View
  • Select activity Lekcije - spisak

    

    Lekcije - spisak File PDF

    Completion

    Students must

    

    View
  • Select activity Lekcije - Upload

    

    Lekcije - Upload Assignment

    Due: Saturday, 20 December 2025, 10:59 PM

    Completion

    Students must

    

    Make a submission