Machine Learning: Syllabus
Prerequisites: Fundamental Algorithms.
Requirements: Projects (50%), Final exam (50%).
- Introduction (Mitchell, chap 1; Witten chaps 1)
- Decision tree learning (M, ch 3; W secs 3.1, 3.2, 4.3, 6.1)
- Learning classification rules (M, ch 2,10; W secs 3.3, 4.4, 6.2)
- Bayesian learning (M ch 6; W sec 4.2)
- Instance-based learning and clustering (M ch 8; W secs 3.8, 4.7, 6.4)
- Evaluation (M ch 5; W ch 5)
- Numerical prediction (W secs 4.6, 6.3, 6.5)
- Learning association rules (W secs 3.4, 4.5)
- Explanation-based learning (M ch 11,12)
- Data preparation (W ch 2)
- Genetic algorithms (M ch 9)
- Computational learning theory (M ch 7)
Programming assignments must be submitted by email. The format should
be the ASCII source file for the code. Be sure to include your name as
a comment at the beginning of the code.
Homeworks must be submitted at or before the beginning of class on the
day due. Assignments will be accepted up to a week late, with a penalty
of one point out of ten. No assignments will be accepted more than a week
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