Introduction to Machine Learning

Fall 2025 • American University of Beirut

Course Schedule

Introduction to Machine Learning • Fall 2025

Introduction

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Sep 1
1
Introduction: What is a Model?
Models, ethics, purpose of AI, and course logistics
75 min Lecture
Course introduction covering learning objectives, expectations, and motivation. Exploration of AI's transformative impact on society and discussion of ethical implications in AI development and deployment.
How to study this session
  • Before class: nothing, come curious
  • After: reread the What is a Model slides, note one model you use daily
  • Set up Python per the logistics page
  • Start PS0 (posted today, due Sep 9)
Lecture
Articles
Sep 3
2
Data, Models, and Python
75 min Lecture
How to study this session
  • Review the slides and run the intro notebook
  • Skim CS229 notes section on supervised learning framing
  • Continue PS0
Lecture
References
  • Data Visualization Example (coming soon) Recommended
Sep 8
3
Math and Python Review - Your Computational Toolkit
Linear algebra meets computational implementation
75 min Lecture
Comprehensive review of mathematical foundations essential for machine learning, combined with hands-on Python implementation. Students will bridge the gap between mathematical notation and computational implementation through interactive notebooks that combine CS229 linear algebra concepts with NumPy code examples.
How to study this session
  • Redo the in-class exercises in the math review notebook
  • Finish and submit PS0 (due Sep 9)
  • Self-test: the linear algebra quiz on the slides
Preparation
Lecture
References

Function Approximation

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Sep 10
4
Supervised Learning and Linear Regression
First machine learning algorithm and foundational concepts
75 min Lecture
Introduction to supervised learning paradigm with linear regression as the first ML algorithm. Mathematical foundations, implementation, and practical applications with hands-on coding.
How to study this session
  • Go through the interactive linear regression slides and try each widget
  • Read CS229 notes Part I, sections 1.1-1.2
  • Start PS1 (posted today)
  • Self-test with the in-slide quizzes
Lecture
References
Sep 15
5
Feature Engineering and Generalization
From raw data to meaningful features, avoiding overfitting
75 min Lecture
Deep dive into feature engineering techniques and understanding generalization. Covers bias-variance tradeoff, cross-validation, and regularization methods.
How to study this session
  • Review generalization slides, focus on bias-variance
  • Run the cross-validation notebook
  • Continue PS1
Lecture
References
Sep 17
6
Classification and Logistic Regression
From regression to classification with probabilistic models
75 min Lecture
Introduction to classification problems using logistic regression. Covers sigmoid function, maximum likelihood estimation, and decision boundaries.
How to study this session
  • Go through the interactive logistic regression slides
  • Compare the MSE vs cross-entropy loss surfaces in the slides
  • Continue PS1 (due Sep 24)
Lecture

Probabilistic Reasoning

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Sep 22
7
Maximum Likelihood Estimation and Generalized Linear Models
Foundations of probabilistic reasoning and statistical learning
75 min Lecture
Deep dive into maximum likelihood estimation and generalized linear models, at the foundation of statistical learning theory.
How to study this session
  • Review MLE slides, rederive the least-squares connection
  • CS229 notes on GLMs
  • Start thinking about PS2
Lecture
Sep 24
8
Generative Learning and Language
Working with Language and the Naive Bayes Algorithm
75 min Lecture
Working with language, inferring algorithms from space v. non-spam, and the Naive Bayes Algorithm as an example for generative learning
How to study this session
  • Review Naive Bayes slides
  • Finish PS1, start PS2
  • Try the language-model exercise from class
Lecture

Deep Learning

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Sep 29
9
Introduction to Neural Networks
From linear models to deep networks
75 min Lecture
Introduction to neural networks, from biological inspiration to mathematical formulation. Covers perceptrons, multilayer networks, and activation functions.
Lecture
Oct 1
10
Backpropagation, Computation Graphs and Hands on
A deeper dive into the inner working of deep learning
75 min Lecture
Backpropagation, chain rule, computation graph, pytorch, tutorial, symmetry
Lecture
References
Oct 6
11
Advanced Neural Networks
Advanced Neural network architectures
75 min Lecture
Advanced deep learning architectures from fully connected networks, to autoencoders, recurrent neural networks, convolutional neural networks, etc. - snapshots from CS231n
Lecture
Oct 8
12
CNNs, RNNs and AI Assistants
How to use AI coding assistants in the context of deep learning
75 min Lecture
How to use AI coding assistants in the context of deep learning
Lecture

Midterm

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Oct 13
13
Midterm Review
A story-format review of the first half
75 min Review
A story-format review of the first half.
Oct 15
14
Midterm Exam
In class, 75 minutes
75 min Exam
In class, 75 minutes.

Unsupervised Learning

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Oct 20
15
Introduction to Unsupervised Learning & K-Means Clustering
Introduction to clustering and unsupervised pattern discovery
75 min Lecture
Introduction to clustering algorithms with focus on K-means. Covers cluster analysis, centroid-based clustering, and evaluation metrics for unsupervised learning.
Lecture
Oct 22
16
Principal Component Analysis
Principal component analysis and Dimensionality Reduction
75 min Lecture
Dimensionality reduction. Focus on Principal Component Analysis (PCA), SVD, and applications to data compression and visualization.
Lecture
Oct 27
17
EM Algorithm and Gaussian Mixture Models
Probabilistic clustering and the EM algorithm
75 min Lecture
Advanced clustering using Gaussian Mixture Models and the Expectation-Maximization algorithm. Covers probabilistic clustering, latent variable models, and maximum likelihood estimation.
Lecture
Oct 29
18
Other Unsupervised Learning Methods
Hierarchical clustering, Autoencoders, Kernel PCA and others
75 min Lecture
Survey of additional unsupervised learning techniques including hierarchical clustering, density-based clustering (DBSCAN), manifold learning, and dimensionality reduction methods.
Lecture
Nov 3
19
Story Session: The Rope as an Instrument
Unsupervised discovery on real physical data
75 min Story
Unsupervised discovery on real physical data.
Lecture

Reinforcement Learning

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Nov 5
20
Reinforcement Learning I
Introduction to RL, MDPs, and value iteration
75 min Lecture
Introduction to reinforcement learning paradigm. Covers Markov Decision Processes (MDPs), value functions, and dynamic programming solutions including value and policy iteration.
Lecture
Nov 10
21
Reinforcement Learning II
Introduction to RL, MDPs, and value iteration
75 min Lecture
Introduction to reinforcement learning paradigm. Covers Markov Decision Processes (MDPs), value functions, and dynamic programming solutions including value and policy iteration.
Lecture

Advanced Topics

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Nov 12
22
Foundation Modeling
Large-scale models and transfer learning
75 min Lecture
Introduction to foundation models - large-scale pre-trained models that can be adapted to various downstream tasks. Covers transfer learning, fine-tuning, and model scaling.
Lecture
Nov 17
23
Machine Learning for Modeling Physical Systems
How does ML apply to science and engineering where some physics-based models are already known?
75 min Lecture
Modeling scientific systems with machine learning' '
Lecture

Projects and Closing

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Nov 19
24
Project Work Session
In-class work on final projects
75 min Project
In-class work on final projects.
Nov 24
25
Closing Story Session and Review
The course in one arc
75 min Story
The course in one arc.
Nov 26
26
Review and Exam Prep
Flipped Q and A session
75 min Review
Flipped Q and A session.
Dec 1
27
Project Presentations I
Final project presentations
75 min Project
Final project presentations.
Dec 3
28
Project Presentations II and Course Close
Final presentations and farewell
75 min Project
Final presentations and farewell.

This schedule is dynamically generated from lecture metadata. Materials and links are updated as they become available.