Machine Learning: Syllabus
Prerequisites: Fundamental Algorithms.
Requirements: Projects (50%), Final exam (50%).
Course topics:
- 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)
Submitting homework:
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
late.
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