Integrating AI and machine learning in software engineering course for high school students 1st Edition by Ahuva Sperling, Dorit Lickerman ISBN

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ISBN 13: 
Author: Ahuva Sperling, Dorit Lickerman

Integrating AI and machine learning in software engineering course for high school students 1st Table of contents:

• Introduction to AI and Machine Learning

  • 1.1 What is Artificial Intelligence (AI)?
  • 1.2 The Evolution of AI and Machine Learning
  • 1.3 AI in Everyday Life: Applications and Examples
  • 1.4 Ethical Considerations in AI and ML

• Foundations of Machine Learning

  • 2.1 Understanding Machine Learning (ML)
  • 2.2 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • 2.3 Key Concepts: Algorithms, Models, and Data
  • 2.4 The Role of Data in Machine Learning

• Programming for AI and ML

  • 3.1 Introduction to Programming with Python
  • 3.2 Libraries and Tools for AI/ML: NumPy, Pandas, Matplotlib
  • 3.3 Introduction to Jupyter Notebooks for Data Science
  • 3.4 Writing Your First ML Model in Python

• Data Preparation and Preprocessing

  • 4.1 Importance of Clean Data
  • 4.2 Collecting and Organizing Data
  • 4.3 Data Cleaning Techniques
  • 4.4 Feature Engineering and Feature Selection
  • 4.5 Normalization and Standardization

• Supervised Learning Algorithms

  • 5.1 Linear Regression
  • 5.2 Classification Algorithms: K-Nearest Neighbors, Decision Trees, and Random Forests
  • 5.3 Evaluating Model Performance: Accuracy, Precision, Recall, F1 Score
  • 5.4 Overfitting and Underfitting

• Unsupervised Learning Algorithms

  • 6.1 Clustering: K-Means and Hierarchical Clustering
  • 6.2 Dimensionality Reduction: PCA (Principal Component Analysis)
  • 6.3 Anomaly Detection
  • 6.4 Evaluating Unsupervised Models

• Reinforcement Learning

  • 7.1 What is Reinforcement Learning?
  • 7.2 The Basics of Agents, Environments, and Rewards
  • 7.3 Simple RL Algorithms: Q-Learning and Deep Q-Networks (DQN)
  • 7.4 Applications of Reinforcement Learning

• Deep Learning and Neural Networks

  • 8.1 Introduction to Neural Networks
  • 8.2 How Neural Networks Learn: Backpropagation and Gradient Descent
  • 8.3 Building Simple Neural Networks with TensorFlow and Keras
  • 8.4 Convolutional Neural Networks (CNNs) for Image Recognition
  • 8.5 Recurrent Neural Networks (RNNs) for Time Series Data

• AI in Software Engineering

  • 9.1 AI in Software Development: Code Generation and Debugging
  • 9.2 Automated Testing with AI
  • 9.3 AI for Predictive Analytics in Software Projects
  • 9.4 Using AI to Improve Code Quality and Maintainability

• Project-Based Learning in AI and Machine Learning

  • 10.1 Developing a Simple ML Project
  • 10.2 Step-by-Step Guide to Building a Classifier
  • 10.3 Building a Chatbot with AI
  • 10.4 Evaluating and Improving Your Project
  • 10.5 Presenting Your ML Project

• Career Paths in AI and Machine Learning

  • 11.1 AI and ML Careers: Overview and Opportunities
  • 11.2 Key Skills and Certifications for AI/ML Careers
  • 11.3 Preparing for College and Advanced Studies in AI
  • 11.4 Interviews and Job Preparation for AI Roles

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