Modeling, Learning, and Decision-Making

Spring 2023
#28681
COMPSCI 189 114 - DIS 114 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
05:00 pm - 05:59 pm
Dwinelle 229
Class #:28681
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 114 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.
Spring 2023
#28680
COMPSCI 189 113 - DIS 113 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
05:00 pm - 05:59 pm
Dwinelle 243
Class #:28680
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 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.
Spring 2023
#28435
COMPSCI 189 112 - DIS 112 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
04:00 pm - 04:59 pm
Genetics & Plant Bio 107
Class #:28435
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 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.
Spring 2023
#28434
COMPSCI 189 111 - DIS 111 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
04:00 pm - 04:59 pm
Wheeler 130
Class #:28434
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 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.
Spring 2023
#28433
COMPSCI 189 110 - DIS 110 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
03:00 pm - 03:59 pm
Hildebrand B56
Class #:28433
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 110 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.
Spring 2023
#28432
COMPSCI 189 109 - DIS 109 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
03:00 pm - 03:59 pm
Social Sciences Building 185
Class #:28432
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 109 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.
Spring 2023
#28431
COMPSCI 189 108 - DIS 108 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
02:00 pm - 02:59 pm
Wheeler 120
Class #:28431
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 108 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.
Spring 2023
#28430
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
01:00 pm - 01:59 pm
Wheeler 20
Class #:28430
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 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.
Spring 2023
#28429
COMPSCI 189 106 - DIS 106 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
01:00 pm - 01:59 pm
Etcheverry 3113
Class #:28429
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 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.
Spring 2023
#28428
COMPSCI 189 104 - DIS 104 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
04:00 pm - 04:59 pm
Hildebrand B56
Class #:28428
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 104 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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