The key to intelligence is the ability to learn. Studies in the field of machine learning attempt to endow computers with this intrinsic capability that exists in all higher-order organisms to one degree or another. Learning can be loosely defined as a process that improves performance of an agent by acquiring knowledge through interactions with a changing environment.

Areas using machine learning techniques vary from automated knowledge acquisition in expert systems to adaptation in robotic control, from data mining in databases to intelligent information retrieval. In the internet age, machine learning algorithms have been used in areas such as search engines (google), spam detection (yahoo mail), and product recommendation (netflix).

We will discuss various machine learning algorithms. Written and programming assignments culminate in a mini-research project for the term.

Textbook:

• Machine Learning, Tom Mitchell, McGraw-Hill, 1997.

Tentative Schedule:

• Introduction (Ch1)
• Concept Learning (Ch2)
• Decision Tree Learning (Ch3)
• Artificial Neural Networks (Ch4)
• Genetic Algorithms (Ch9)
• Rule Learning (Ch10)
• Instance-Based Learning (Ch8)
• Bayesian Learning (Ch6)

Important Dates (Assignment due dates might be adjusted due to circumstances)

Evaluation/Grading:

• Assignments (50%) and term project (20%)
• Final exam (30%)

Prerequisites:

• Artificial Intelligence (CSE 4301/5290) or Analysis of Algorithms (CSE 4081/5211) [or consent of the instructor].
• Basic probability and statistics.
• Proficient knowledge in data structures, algorithms, and programming.

Policies

• Students are encouraged to help each other on assignments, but plagiarism (copying) is prohibited.
  • first violation: zero on assignment/test
  • second violation: 'F' for the course
• Late assignments are accepted, but 20% is deducted for each day.
• Documentation constitutes 10% of each programming assignment.
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