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Intelligent Systems

3 Credits | 200 Level | 38 Contact hours

Probabilistic Machine Learning : an Introduction (Murphy, Kevin P.)
Artificial intelligence : a modern approach (Russell, Stuart J | Norvig, Peter)

As indicated in the objectives of the course, it will contribute to the student's training the ability to face problems that require the development of intelligent systems based on artificial intelligence. Nowadays, the training provided in the course is highly demanded at a professional level in the labor market.

Topic 1. Intelligent Systems: Representation and Search
1. Introduction to AI. Concepts, evolution, areas and applications.
2. Uninformed search
3. Informed search: heuristics, algorithm A*.
4. A* methods with limited memory
5. Search with adversary.
6. Rule-based systems (RBS), components and architecture. CLIPS. Pattern-matching.
7. Inference in SBR: chaining and control. Inference engine.

Topic 2. Intelligent Systems: Machine Learning
1. Introduction to machine learning
2. Probabilistic reasoning: Bayes' rule.
3. Supervised learning: logistic regression
4. Supervised learning: classification trees.
5. Non-supervised learning: k-means algorithm
7. Collision Detection
8. Maps and Paths

Upon successful completion of this course, students will be able to:

• To reason abstractly, analytically and critically, knowing how to elaborate and defend arguments in their area of study and professional field.
• Find relevant information from different sources and investigate technological developments in your field of work and in related areas.
• Have the ability to apply the fundamental principles and basic techniques of the intelligent systems and their practical implementation.
• Solve problems in artificial intelligence using knowledge-based systems, heuristic search and adversarial search.
• Solve problems in machine learning using supervised (logistic regression, decision trees, etc.) and unsupervised learning.

Class Attendance 5%
Assignments 25%
Midterm Exam 35%
Final Exam 35%