Modeling, Learning, and Decision-Making

2023 Fall
#29190
COMPSCI 189 104 - DIS 104 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
10:00 am - 10:59 am
Wheeler 120
Class #:29190
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.
2023 Fall
#29189
COMPSCI 189 103 - DIS 103 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Th
09:00 am - 09:59 am
Dwinelle 223
Class #:29189
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.
2023 Fall
#28661
COMPSCI 189 102 - DIS 102 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
06:00 pm - 06:59 pm
Hildebrand B51
Class #:28661
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.
2023 Fall
#28660
COMPSCI 189 101 - DIS 101 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
12:00 pm - 12:59 pm
Cory 540AB
Class #:28660
Units:4

Instruction Mode: In-Person Instruction

No Open 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.
2023 Fall
#28513
COMPSCI 189 999 - DIS 999 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
12:00 am
Class #:28513
Units:4

Instruction Mode: In-Person Instruction

Open Seats

18 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.
2023 Fall
#28452
COMPSCI 189 110 - DIS 110 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
Th
10:00 am - 10:59 am
Wheeler 200
Class #:28452
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.
2023 Fall
#28393
COMPSCI 189 109 - DIS 109 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 120
Class #:28393
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.
2023 Fall
#28118
COMPSCI 189 108 - DIS 108 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 23, 2023 - Dec 08, 2023
We
01:00 pm - 01:59 pm
Cory 540AB
Class #:28118
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 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.
2023 Fall
#28085
COMPSCI 189 001 - LEC 001 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jennifer Listgarten, Jitendra Malik
Aug 23, 2023 - Dec 08, 2023
Tu, Th
02:00 pm - 03:29 pm
Dwinelle 155
Class #:28085
Units:4

Instruction Mode: In-Person Instruction

Open Seats

18 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.
Spring 2024
#34418
COMPSCI 189 122 - DIS 122 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 16, 2024 - May 03, 2024
Tu
07:00 pm - 07:59 pm
Dwinelle 242
Class #:34418
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.
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