Artificial Intelligence Course ( Basic)

|ITA

Week 1: Introduction to AI Lesson 1: What is Artificial Intelligence?

• Definition and history of AI
• Differences between AI, Machine Learning (ML), and Deep Learning (DL)

Lesson 2: Applications of AI

• Real-world examples and case studies (e.g., voice recognition, computer vision)
• Ethical and social implications of AI

Activities:

• Discussion of case studies and reflection on the social impact of AI
• Introductory quiz on key concepts

Week 2: Python Fundamentals and AI Tools Lesson 1: Introduction to Python

• Installation and setup of the development environment
• Basic syntax and data structures (lists, dictionaries, tuples)

Lesson 2: Python Libraries for AI

• Introduction to NumPy, Pandas, and Matplotlib
• Overview of scikit-learn

Activities:

• Practical exercises in Python and data manipulation
• Simple data visualizations with Matplotlib

Week 3: Basic Mathematics for AI Lesson 1: Linear Algebra

• Vectors and matrices, fundamental operations

Lesson 2: Probability and Statistics

• Basic concepts: distributions, mean, variance

Activities:

• Exercises on algebraic operations and descriptive statistics
• Application of concepts to real datasets

Week 4: Introduction to Machine Learning Lesson 1: Fundamental Concepts

• Supervised vs unsupervised learning
• Overfitting and underfitting

Lesson 2: Types of Algorithms

• Linear and logistic regression
• Classification and clustering

Activities:

• Implementation of linear regression with scikit-learn
• Classification and clustering exercises on sample datasets

Week 5: Regression and Classification Lesson 1: Linear Regression

• Model, cost function, and optimization

Lesson 2: Classification Algorithms

• k-Nearest Neighbors (k-NN), Support Vector Machines (SVM)

Activities:

• Regression and classification projects on real datasets
• Model evaluation with appropriate metrics

Week 6: Clustering Techniques and Dimensionality Reduction Lesson 1: Clustering

• k-means algorithm, DBSCAN

Lesson 2: Dimensionality Reduction

• PCA (Principal Component Analysis)

Activities:

• Implementation of clustering on unlabeled data
• Application of PCA for data visualization

Week 7: Introduction to Deep Learning Lesson 1: Basic Neural Networks

• Architecture of perceptrons, activation functions

Lesson 2: Deep Learning Frameworks

• Overview of TensorFlow and Keras

Activities:

• Building and training a simple neural network
• Experimentation with TensorFlow/Keras on a basic dataset

Week 8: Final Project and Discussion on the Future of AI Lesson 1: Final Project Development

• Guide to creating a complete AI project
• Tips for project presentation and documentation

Lesson 2: The Future of AI

• Emerging trends, generative AI, and ethical implications

Activities:

• Presentation and discussion of final projects
• Reflection on future challenges and opportunities in AI

Additional Resources and Activities: Recommended Readings:

• “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig
• “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Online Courses and Tutorials:

• Coursera, edX, Udacity for specific deep dives

Forums and Communities:

• Participation in online communities like Stack Overflow, Reddit, GitHub

Notes:

• Each week includes practical exercises, quizzes, and discussions to facilitate learning and understanding of the concepts.
• The course can be adjusted based on student needs and available resources.
• This program provides a solid foundation for understanding AI fundamentals and prepares students for more advanced topics in the future.