Introduction to Machine Learning

Fall 2025 • American University of Beirut

Course Schedule

Introduction to Machine Learning • Fall 2025

Introduction

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Aug 26
1
Introduction, Ethics/Purpose of AI, Logistics
Course foundations and 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.
Lecture
Articles
Aug 28
2
Data, Models, and Python
75 min Lecture
Lecture
References
  • Data Visualization Example (coming soon) Recommended
Sep 2
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.
Preparation
Lecture
References

Function Approximation

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Sep 9
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.
Lecture
References
Sep 11
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.
Lecture
References
Sep 16
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.
Lecture

Probabilistic Reasoning

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Sep 18
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.
Lecture
Sep 23
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
Lecture

Deep Learning

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Sep 30
10
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 2
11
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 7
12
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 9
13
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

Unsupervised Learning

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Oct 14
14
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 16
15
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 21
16
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 23
17
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

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