Course
AI: theory and teaching methods in primary and high school
Advanced training course βArtificial Intelligence: Theory and Methods of Teaching in Basic and High Schoolβ, with a total labor intensity of 72 academic hours.
The training program was developed and implemented with the participation of experts from the Academy of Artificial Intelligence for schoolchildren and experts from the Deep Learning School, one of the FPMI projects for schoolchildren in the field of artificial intelligence and machine learning.
What will we learn on the course?
Module
Introduction to Artificial Intelligence
Module
Machine learning and methods for forming basic concepts
Module
Neural networks and methods for forming basic concepts
Module
Python Programming Language Basics
Module
Data Analytics in Python
Module
Introduction to Machine Learning
Module
Machine learning algorithms
Module
Introduction to Neural Networks
Module
Convolutional Neural Networks
Module
Natural Language Processing
The road to AI
Start teaching AI in elementary and high school
Curriculum
Introduction to Artificial Intelligence
Machine learning and methods for forming basic concepts
Neural networks and methods for forming basic concepts
Programming Language Basics
Data Analytics in Python
Introduction to Machine Learning
Machine learning algorithms
Introduction to Neural Networks
Convolutional Neural Networks
Natural Language Processing
Introduction to Artificial Intelligence
In this block we will talk about the conceptual foundations of artificial intelligence and methods of teaching data analysis and artificial intelligence at school. We will give an overview of the trends in artificial intelligence, and also discuss ethical and social issues related to artificial intelligence.
Section structure
1
Conceptual foundations of teaching artificial intelligence in primary school
2
Basic concepts and definitions of the course
3
Computer vision: history of development and areas of application
4
Visual pattern recognition
5
Natural language processing
6
Introduction and development of the concepts βArtificial Intelligenceβ and βIntelligent Systemsβ
7
Prospects for the development of artificial intelligence
Machine learning and methods for forming basic concepts
The module is intended for implementation in the school computer science curriculum in high school (grades 7-9). We will discuss the basic concepts of machine learning, with a particular focus on data as the fuel for building artificial intelligence models. We will study the basic stages of working with data and learn how to carry out simple operations with data using spreadsheets.
Section structure
1
Introduction to Data Analysis
2
Types of data in machine learning
3
Solving the machine learning problem
4
Data dependencies. Machine learning problem statement and linear models
5
Finding dependencies in data in Excel
6
Methodological features of classroom and extracurricular activities on the topic
Neural networks and methods for forming basic concepts
The module is intended for implementation in the school computer science curriculum in high school (grades 7-9). We will discuss neural networks as the main engine of modern artificial intelligence, talk about the applications of neural networks, introduce the concept of neuron and neural network, and conduct experiments using an interactive environment to demonstrate the capabilities of neural networks.
Section structure
1
Concept of a neural network
2
Multilayer perceptron
3
Consideration of a platform for building neural networks
4
Principle of training neural networks
5
Training practice and selection of neural network parameters
6
Methodological features of classroom and extracurricular activities on the topic
Programming Language Basics
This module is an introduction to the Python language. It is one of the most popular languages today.
Section structure
1
Introduction to the Python programming language
2
Introduction to functions
3
Function arguments
4
Data types
5
Mathematical operators
6
Logical expressions
7
Conditional operator
8
Logical operations
9
Cascading and conditional operator
10
Loops and their lines
11
While loop
12
For loop
13
Lists
14
Application of lists in real problems
15
Dictionaries
16
Introduction to functions
17
Local and global changes
18
Anonymous functions
19
Methodological features of classroom and extracurricular activities on the topic
Data Analytics in Python
The second step in learning Python for data analysis is to move on to learning libraries for working with data.
Section structure
1
The need to visualize data for analysis
2
Introduction to the Pandas library
3
Matplotlib - Pandas' main helper
4
Obtaining general information about the data
5
Indexing by conditions and changing data in tables
6
Data visualization
7
Introduction to the Numpy library
8
Slicing and multidimensional arrays
9
Methodological features of classroom and extracurricular activities
Introduction to Machine Learning
In this block, we talk in detail about machine learning as the main method of modern artificial intelligence.
Section structure
1
Machine learning is all around us
2
Basic concepts and tasks of machine learning
3
The problem of retraining and quality criteria
4
k nearest neighbors algorithm
5
Machine learning pipeline
6
Introducing the sklearn library and training KNN
7
Solving an applied machine learning problem
8
8 Methodological features in class and extracurricular activities on the topic
Machine learning algorithms
In this module we will study the main machine learning algorithms: k-nearest neighbors, linear algorithms, as well as decision trees and compositions of algorithms. Each of these algorithms is designed to solve different machine learning problems and has its own advantages and disadvantages, which we will also discuss in this block.
Section structure
1
Statement of the linear regression problem
2
Linear regression training
3
Solving an applied problem using linear regression
4
Statement of the linear classification problem
5
Logistic loss function
6
Construction and training of decision trees
7
Analysis of decision trees
8
Compositions of algorithms. Bagging and random forest
9
Methodological features in class and extracurricular activities on the topic
Introduction to Neural Networks
In the module we analyze neural networks, while raising much more complex questions regarding the structure of neural networks. In addition, great importance is given to the practice of neural networks using the Pytorch library β one of the most common professional libraries for building neural networks, which, nevertheless, is suitable for study by strong students.β―
Section structure
1
History of the development of neural networks
2
One neuron and a fully connected neural network
3
Training fully connected neural networks
4
Introduction to the Pytorch library
5
Practice of building a neural network
6
Methodological features in class and extracurricular activities on the topic
Convolutional Neural Networks
The module is devoted primarily to the applications of neural networks in computer vision for the task of image classification.
Section structure
1
History of the development of computer vision
2
The idea of how convolutional neural networks work
3
Convolution operation
4
Convolutional neural network
5
Interpretation of convolutional layers
6
Pooling operation
7
Operation pooling, padding
8
Loading data and building a neural network
9
Training a neural network
10
Methodological features in class and extracurricular activities on the topic
Natural Language Processing
In the module we will study both classical and neural network methods of working with texts. You will learn more about these areas later in this module.
Section structure
1
Introduction to natural language processing. NLP tasks
2
Vector representations of words
3
Word2Vec algorithm and vector arithmetic
4
Introduction to text classification
5
Recurrent neural networks for text classification
6
Text preprocessing
7
Classification of texts
8
Recurrent neural networks
9
Methodological features in class and extracurricular activities on the topic
F.A.Q.
What is the volume and duration of the course?
The course consists of 72 academic hours and is recommended to be completed within 3 months.
Who is this course suitable for?
This course is suitable for you if:
- You are a teacher of a general education or specialized school, a teacher of additional education
- You are interested in conducting classes on the basics of AI and ML and are ready to use the course materials for this
- Youβve got knowledge in computer science/mathematics/programming
What class can I teach after completing the course?
The first 3 modules are designed for primary school students, the remaining modules are recommended for high school students.
Project organizer: Charitable Foundation βInvestment to the Futureβ, OGRN 1157700017518
The Academy of Artificial Intelligence for Schoolchildren is not an educational service subject to licensing and does not imply the issuance of a state certificate
Do you have any questions or suggestions?
For schoolchildren, teachers and parents
For press
Β© 2018β
2024 Β«AI-ACADEMYΒ»