# 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)

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)

Prerequisite: Approval of the Academic Head or nominee