Modeling, Learning, and Decision-Making

Spring 2022
#22110
COMPSCI 189 116 - DIS 116 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
We
12:00 pm - 12:59 pm
Hildebrand B51
Class #:22110
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 116 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 2022
#22109
COMPSCI 189 115 - DIS 115 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
We
12:00 pm - 12:59 pm
Hildebrand B56
Class #:22109
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 115 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 2022
#22108
COMPSCI 189 114 - DIS 114 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
05:00 pm - 05:59 pm
Dwinelle 229
Class #:22108
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 2022
#22107
COMPSCI 189 113 - DIS 113 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
05:00 pm - 05:59 pm
Dwinelle 243
Class #:22107
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 2022
#21846
COMPSCI 189 112 - DIS 112 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
04:00 pm - 04:59 pm
Social Sciences Building 185
Class #:21846
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 2022
#21845
COMPSCI 189 111 - DIS 111 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
04:00 pm - 04:59 pm
Genetics & Plant Bio 103
Class #:21845
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 2022
#21844
COMPSCI 189 110 - DIS 110 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
03:00 pm - 03:59 pm
Social Sciences Building 185
Class #:21844
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 2022
#21843
COMPSCI 189 109 - DIS 109 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
03:00 pm - 03:59 pm
Hildebrand B56
Class #:21843
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 2022
#21842
COMPSCI 189 108 - DIS 108 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
02:00 pm - 02:59 pm
Cory 289
Class #:21842
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 2022
#21841
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
01:00 pm - 01:59 pm
Requested General Assignment
Class #:21841
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.
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