CIS 210 - Introduction to Machine Learning

1. Understanding ML Fundamentals:

   • Develop a foundational understanding of Machine Learning (ML), its principles, and key components.

2. AI Model Classification:

   • Classify and differentiate between supervised, unsupervised, and reinforcement learning AI model types.

3. Mathematical Foundations of ML:

   • Explore the mathematical foundations underpinning various ML models for a deeper comprehension.

4. Implementing ML Models in Python:

   • Gain practical proficiency in implementing classification and regression ML models using Python programming.

5. Model Differentiation and Selection:

   • Distinguish ML models based on their mathematical workings, enabling informed model selection.

6. Decoding Decision Trees:

   • Understand the two types of decision trees used in ML and their role in effective data classification.

7. Clustering Algorithms Mastery:

   • Delve into the working mechanisms of different clustering algorithms and their applications.

8. Applications of Unsupervised Learning:

   • Explore a variety of applications demonstrating the relevance and significance of unsupervised learning in real-world scenarios.

By achieving these learning outcomes, participants will gain a solid foundation in Machine Learning (ML), enabling them to understand, implement, and leverage various ML models for practical applications.

Course Outline: Fundamentals of Machine Learning

Module 1: Introduction to Machine Learning (ML)

• 1.1 Overview of Machine Learning
  • Exploring ML concepts and its relevance in today’s world
• 1.2 History and Evolution
  • Tracing the historical development and evolution of Machine Learning

Module 2: Machine Learning Fundamentals

• 2.1 Basic Concepts and Terminology
  • Introduction to fundamental terms and concepts in Machine Learning
• 2.2 Supervised, Unsupervised, and Reinforcement Learning
  • Exploring different AI model types and their distinctions

Module 3: Mathematical Foundations of ML Models

• 3.1 Linear Algebra for ML
  • Exploring linear algebra concepts crucial for ML models
• 3.2 Statistics in ML
  • Understanding how statistics is applied in optimizing ML models

Module 4: Implementing ML Models with Python

• 4.1 Python Basics for ML
  • Reviewing Python basics relevant to implementing ML models
• 4.2 Classification ML Models
  • Implementing and hands-on exercises on classification models in Python and no-code tools
• 4.3 Regression ML Models
  • Implementing and practical exercises on regression models using Python and no-code tools

Module 5: Understanding Decision Trees and Clustering

• 5.1 Types of Decision Trees in ML
  • Detailed exploration of decision tree types and their application in classification
• 5.2 Clustering Algorithms
  • In-depth exploration of clustering algorithms and their use in unsupervised learning

Module 6: Applications and Projects

• 6.1 Practical Applications of ML
  • Analyzing real-world applications of Machine Learning in various domains
• 6.2 Course Project: ML Model Implementation
  • Engaging in a guided project to implement an ML model using Python

Module 7: Recap and Future Directions

• 7.1 Recap of Key Concepts
  • Summarizing the fundamental concepts and learnings from the course
• 7.2 Future Trends and Advancements
  • Discussing emerging trends and potential future directions in Machine Learning

Facilitated Discussion 20%-30%

Facilitated Group Work 40%-50%

 
English Prerequisite(s): None
Math Prerequisite(s): Eligible for Math 105 or Higher; SAT Math Score of 24.5 or Higher
Course Corerequisite(s): None
Course-Specific Placement Test: None
Course Aligned with IRW: NA
Consent to Enroll in Course: No Department Consent Required