Modeling, Learning, and Decision-Making

Spring 2023
#28427
COMPSCI 189 101 - DIS 101 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
10:00 am - 10:59 am
Wheeler 220
Class #:28427
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 101 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
#28426
COMPSCI 189 001 - LEC 001 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jonathan Shewchuk
Jan 17, 2023 - May 05, 2023
Mo, We
06:30 pm - 07:59 pm
Hearst Field Annex A1
Class #:28426
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

26 Unreserved Seats

COMPSCI 189 - LEC 001 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.
2022 Fall
#33401
COMPSCI 189 111 - DIS 111 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
Th
01:00 pm - 01:59 pm
Dwinelle 209
Class #:33401
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.
2022 Fall
#29195
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
We
01:00 pm - 01:59 pm
Wheeler 220
Class #:29195
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.
2022 Fall
#29194
COMPSCI 189 106 - DIS 106 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
We
12:00 pm - 12:59 pm
Etcheverry 3119
Class #:29194
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.
2022 Fall
#29193
COMPSCI 189 105 - DIS 105 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
We
11:00 am - 11:59 am
Hearst Gym 242
Class #:29193
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.
2022 Fall
#29192
COMPSCI 189 104 - DIS 104 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
We
10:00 am - 10:59 am
Wheeler 120
Class #:29192
Units:4

Instruction Mode: In-Person Instruction

Open Seats

39 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.
2022 Fall
#29191
COMPSCI 189 103 - DIS 103 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
Th
09:00 am - 09:59 am
Wheeler 30
Class #:29191
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.
2022 Fall
#28041
COMPSCI 189 110 - DIS 110 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
Th
10:00 am - 10:59 am
Etcheverry 3109
Class #:28041
Units:4

Instruction Mode: In-Person Instruction

Open Seats

39 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.
2022 Fall
#27978
COMPSCI 189 109 - DIS 109 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 24, 2022 - Dec 09, 2022
We
11:00 am - 11:59 am
Etcheverry 3111
Class #:27978
Units:4

Instruction Mode: In-Person Instruction

Open Seats

36 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.
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