Modeling, Learning, and Decision-Making

Spring 2023
#29213
COMPSCI 189 105 - DIS 105 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
06:00 pm - 06:59 pm
Wheeler 200
Class #:29213
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 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.
Spring 2023
#29126
COMPSCI 189 122 - DIS 122 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
06:00 pm - 06:59 pm
Soda 310
Class #:29126
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 122 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
#29125
COMPSCI 189 121 - DIS 121 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
03:00 pm - 03:59 pm
Soda 405
Class #:29125
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 121 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
#29056
COMPSCI 189 103 - DIS 103 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
04:00 pm - 04:59 pm
Social Sciences Building 175
Class #:29056
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 103 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
#29055
COMPSCI 189 102 - DIS 102 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
03:00 pm - 03:59 pm
Moffitt Library 106
Class #:29055
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 102 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
#28925
COMPSCI 189 120 - DIS 120 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
11:00 am - 11:59 am
Wheeler 20
Class #:28925
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
COMPSCI 189 - DIS 120 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
#28924
COMPSCI 189 119 - DIS 119 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
10:00 am - 10:59 am
Wheeler 220
Class #:28924
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 119 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
#28748
COMPSCI 189 999 - DIS 999 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
12:00 am
Class #:28748
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

26 Unreserved Seats

COMPSCI 189 - DIS 999 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
#28683
COMPSCI 189 116 - DIS 116 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
04:00 pm - 04:59 pm
Genetics & Plant Bio 107
Class #:28683
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 2023
#28682
COMPSCI 189 115 - DIS 115 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
We
12:00 pm - 12:59 pm
Genetics & Plant Bio 103
Class #:28682
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.
Subscribe to Modeling, Learning, and Decision-Making