Syllabus Overview
Artificial Intelligence & Machine Learning Course Syllabus
Course Overview
This course introduces the foundational concepts of Artificial Intelligence (AI) and Machine Learning (ML). It covers theory, algorithms, and real-world applications, giving students the knowledge to understand and implement AI and ML solutions. Students will learn about supervised, unsupervised, and reinforcement learning, as well as AI frameworks and tools.
Course Objectives
By the end of the course, students will:
- Understand key AI and ML concepts
- Be familiar with supervised, unsupervised, and reinforcement learning
- Gain hands-on experience with ML algorithms
- Learn to evaluate model performance and tune hyperparameters
- Understand ethical considerations in AI/ML
Course Structure
Week Topics
1. Introduction to Artificial Intelligence
- History of AI
- Key AI concepts and applications
- Differences between AI, Machine Learning, and Deep Learning
2. Introduction to Machine Learning
- Overview of ML types: Supervised, Unsupervised, and Reinforcement Learning
- ML use cases and real-world applications
3. Linear Algebra & Probability for Machine Learning
- Vectors, matrices, and operations
- Probability and statistics concepts
- Python for basic math operations
4. Supervised Learning: Regression
- Linear Regression
- Polynomial Regression
- Evaluation Metrics (RMSE, R²)
5. Supervised Learning: Classification
- Logistic Regression
- Support Vector Machines (SVMs)
