Search Course Outline
9 course outlines found
1
COMPSCI 760
: Advanced Topics in Machine Learning2024 Semester Two (1245)
An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
2
COMPSCI 760
: Advanced Topics in Machine Learning2024 Semester One (1243)
An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
3
COMPSCI 760
: Advanced Topics in Machine Learning2023 Semester Two (1235)
An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
4
COMPSCI 760
: Advanced Topics in Machine Learning2023 Semester One (1233)
An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
5
COMPSCI 760
: Machine Learning2022 Semester Two (1225)
An overview of the learning problem and the view of learning by search. Covers techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
6
COMPSCI 760
: Datamining and Machine Learning2021 Semester Two (1215)
An overview of the learning problem and the view of learning by search. Techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
7
COMPSCI 760
: Datamining and Machine Learning2021 Semester One (1213)
An overview of the learning problem and the view of learning by search. Techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
8
COMPSCI 760
: Datamining and Machine Learning2020 Semester Two (1205)
An overview of the learning problem and the view of learning by search. Techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
9
COMPSCI 760
: Datamining and Machine Learning2020 Semester One (1203)
An overview of the learning problem and the view of learning by search. Techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee