Intelligent Systems
Semester: Fall 2025 Status: Completed
Course Information
- Start Date: August 25, 2025
- End Date: December 15, 2025
- Time Zone: America/Chicago
- Syllabus: View on Canvas
Description
Introduction to artificial intelligence including search algorithms, game playing, constraint satisfaction, Bayesian networks, probabilistic reasoning, and reinforcement learning.
Topics
- Search algorithms and game playing
- Constraint satisfaction problems
- Probabilistic reasoning
- Bayesian networks
- Machine learning fundamentals
- Reinforcement learning
Resources
- Course Blog Posts
- Canvas Course: Course ID 58606
Assignments
Intelligent Systems – Assignment 3 Problem Solving
Intelligent Systems – Project 3: Reinforcement Learning
Intelligent Systems – Assignment 4 Problem Solving
Intelligent Systems – Assignment 4 Problem Solving Solutions
Intelligent Systems – Assignment 4 Problem Solving Solutions
PDF Materials:
Related Content
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Recent Posts
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Logistic Regression and Softmax: Probabilistic Linear Models for Classification
14 min read
Upgrade the perceptron into a calibrated probabilistic classifier with logistic regression and its multiclass softmax...
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The Perceptron Algorithm: Introduction to Linear Classifiers and Neural Networks
13 min read
Transition from probabilistic to discriminative models with the Perceptron algorithm, learning how linear classifiers use...
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Laplace Smoothing and Maximum Likelihood Estimation in Naive Bayes
12 min read
Learn how to handle zero-frequency problems and overfitting in Naive Bayes through Laplace smoothing and...
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Naive Bayes Classification: From Bayesian Networks to Supervised Learning
14 min read
Learn how Naive Bayes bridges probabilistic reasoning and machine learning, using conditional independence assumptions to...
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Variable Elimination: Efficient Probabilistic Inference in Bayesian Networks
13 min read
Learn how variable elimination dramatically reduces computational complexity in Bayesian network inference through strategic factor...
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Bayesian Networks: D-Separation and Probabilistic Inference
11 min read
Master d-separation algorithms for determining conditional independence in Bayesian networks and understand the foundation of...