Introduction to Machine Learning
Based on:
Hands-On Machine Learning with Scikit-Learn and PyTorch (Aurélien Géron)
Ibrahim Said Ahmad, PhD
Week 1: Introduction to Machine Learning
Meet Your Instructor: Ibrahim Said Ahmad, PhD
Bridging AI research with practical education to empower the next generation of computing professionals
Academic Leadership and Global Experience
Assistant Professor
UW–Stevens Point
2025 - Present
Leading curriculum development and student mentorship in the Department of Computing and New Media Technologies.
Postdoctoral Research Fellow
Northeastern University
2023 - 2025
Conducting advanced research at the Institute for Experiential AI on NLP and computational social science.
Lecturer (~Assistant Professor)
Bayero University Kano
2014 - 2023
Teaching AI, Machine Learning, and Database Systems while mentoring graduate research projects.
Research at the Frontier of AI and Society
Natural Language Processing
Specializing in NLP techniques that address real-world challenges in understanding and processing human language.
Computational Social Science
Applying computational methods to understand social phenomena through sentiment analysis, hate speech detection, and emotion analysis.
Low-Resource Languages
Dedicated to addressing linguistic and cultural disparities in AI systems through dataset creation and cultural adaptation.
Award-Winning Research and Global Initiatives
Competitive Grants
Research Awards
Global Initiatives
Hands-On Education
Expert instruction in Artificial Intelligence, Machine Learning, and Programming with emphasis on practical application.
Mentorship Through Research
Guiding students through research projects and real-world problem solving to develop critical thinking skills.
Student Success
Committed to empowering students with technical skills and knowledge needed for the modern AI landscape.
Teaching Philosophy: Bridging Theory and Practice
About the Course
What You'll Learn
Machine learning fundamentals
Core concepts, terminology, and ML categories
Python ML workflows
Data preprocessing, feature engineering, and complete pipelines
Classical ML algorithms
Classification, regression, clustering, and ensemble methods
Model evaluation
Interpret outputs, use metrics, and design ethical AI systems
Course Structure
20 points
Assignments
5 assignments on AI concepts
40 points
Course Project
Build an AI application
30 points
Exams & Quizzes
4 quizzes + midterm + final
10 points
Participation
Active engagement in class
Total: 100 points
Academic Integrity
All work must be original and individual
Unless collaboration is explicitly allowed in assignment instructions
AI Use Policy
Using third-party resources
Properly cite any code or materials you incorporate. Failure to cite is academic misconduct.
Late Submission Policy
25%
Late Penalty
25% of total points deduction per day late submission penalty
0
Points Awarded
For submissions more than 4 days late
Request extensions BEFORE the due date for legitimate reasons
Course Topics
Weeks 1-4
ML landscape & end-to-end projects
Weeks 5-6
Classification & model evaluation
Weeks 7-8
Linear models & regularization
Week 9
Support Vector Machines
Weeks 10
Decision Trees
Weeks 11
Ensemble methods (Random Forest, Boosting)
Weeks 12-13
Dimensionality reduction & clustering
Weeks 14-16
Integration, final project & presentations
Tools & Keys to Success
Tools We'll Use
Keys to Success
Attend class regularly
Start assignments early
Ask questions when stuck
Practice coding between classes
Python
Programming language
Google Collab
Free development environment
ML Libraries
NumPy, Pandas, Scikit-Learn, Matplotlib
Why This Course?
Goal: learn how ML systems are designed, trained, and evaluated
Learning Objectives
By the end of this week, you should be able to:
Define machine learning formally
Explain when ML is preferable to traditional programming
Classify ML systems
Describe a typical ML workflow
Identify common ML pitfalls
Understand model evaluation basics
[Machine learning is the] field of study that gives computers the ability to learn
without being explicitly programmed.
—Arthur Samuel, 1959
A computer program is said to learn from experience E with respect to some task
T and some performance measure P, if its performance on T, as measured by P,
improves with experience E.
—Tom Mitchell, 1997
What Is Machine Learning?
A program learns from:
Formal Definition (Tom Mitchell)
Example:
Key ML Terminology
Training Set
Training instance (sample)
Feature
Model
dataset
Accuracy (example metric)
Evaluation
Learning requires:
What ML Is Not
Traditional programming struggles when:
Why Use Machine Learning?
Figure 1-1. The traditional approach
Traditional Programming Approach
Figure 1-2. ”The machine learning approach
Machine Learning Approach
Figure 1-3. Automatically adapting to change
Adapting to Change
Examples:
ML for Hard Problems
Figure 1-4. Machine learning can help humans learn
ML Can Help Humans Learn
ML is useful for:
Summary: Why ML?
Examples of ML Applications
Three major axes:
How Can We Classify ML Systems?
Examples:
Figure 1-5. A labeled training set for spam classification
Supervised Learning
Figure 1-6. A regression problem: predict a value, given an input feature
Regression Example
Examples:
Unsupervised Learning
Figure 1-7. Clustering
Clustering
Figure 1-8. Example of a t-SNE visualization highlighting semantic clusters
Visualization & Dimensionality Reduction
Figure 1-9. Anomaly detection
Anomaly Detection
Figure 1-10. Semi-supervised learning with two classes
Semi-Supervised Learning
Figure 1-11. Self-supervised learning example: input (left) and target (right)
Self-Supervised Learning
Figure 1-12. Reinforcement learning
Reinforcement Learning
Batch Learning
Figure 1-13. In online learning, a model is trained and launched into production, and then it keeps learning as new data comes in
Online Learning
Figure 1-14. Using online learning to handle huge datasets
Out-of-Core Learning
Instance-Based Learning
Figure 1-15. Instance-based learning: in this example we consider the class of the three nearest neighbors in the training set
Instance-Based Learning
Figure 1-16. Model-based learning
Model-Based Learning
A Typical ML Workflow
Figure 1-17. Do you see a trend here?
Example: GDP vs Life Satisfaction
Figure 1-18. A few possible linear models
Model Selection
Training
Figure 1-19. The linear model that fits the training data best
Training & Inference
Inference
Two big categories:
Main ML Challenges
Figure 1-21. The importance of data versus algorithms
Insufficient Training Data
Figure 1-22. A more representative training sample
Non-Representative Data
Poor-Quality Data
Image source: https://markkolier.com/wp-content/uploads/2016/11/bad-data1.png
Overfitting
Overfitting and Underfitting
Underfitting
Balance
Figure 1-24. Regularization reduces the risk of overfitting
Regularization
Training vs Test Set
Figure 1-25. Model selection using holdout validation
Validation Set
Figure 1-26. Train-Dev Strategy
Data Mismatch
No Free Lunch Theorem
Big Picture Summary
Next week:
What’s Next?
Reading & Homework