Modeling, Learning, and Decision-Making

COMPSCI 189 (2020-01-14 - 2021-08-18)

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

COMPSCI 189 (2014-01-14 - 2017-05-22)

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#33842
COMPSCI 189 113 - DIS 113 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Th
10:00 am - 10:59 am
Stanley 179
Class #:33842
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 113 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#33841
COMPSCI 189 112 - DIS 112 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Tu
06:00 pm - 06:59 pm
Etcheverry 3119
Class #:33841
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 112 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#33840
COMPSCI 189 111 - DIS 111 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Tu
04:00 pm - 04:59 pm
Etcheverry 3105
Class #:33840
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 111 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#29571
COMPSCI 189 801 - DIS 801 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Tu
08:00 pm - 08:59 pm
Soda 306
Class #:29571
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 801 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#29193
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
01:00 pm - 01:59 pm
Wheeler 202
Class #:29193
Units:4

Instruction Mode: In-Person Instruction

Open Seats

39 Unreserved Seats

COMPSCI 189 - DIS 107 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#29192
COMPSCI 189 106 - DIS 106 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
12:00 pm - 12:59 pm
Etcheverry 3119
Class #:29192
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 106 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2023 Fall
#29191
COMPSCI 189 105 - DIS 105 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
11:00 am - 11:59 am
Wheeler 220
Class #:29191
Units:4

Instruction Mode: In-Person Instruction

Open Seats

36 Unreserved Seats

COMPSCI 189 - DIS 105 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
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