Modeling, Learning, and Decision-Making

Spring 2024
#15823
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 16, 2024 - May 03, 2024
Tu
02:00 pm - 02:59 pm
Wheeler 202
Class #:15823
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 2024
#15822
COMPSCI 189 106 - DIS 106 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 16, 2024 - May 03, 2024
Tu
01:00 pm - 01:59 pm
Social Sciences Building 185
Class #:15822
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 2024
#15821
COMPSCI 189 104 - DIS 104 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 16, 2024 - May 03, 2024
We
01:00 pm - 01:59 pm
Hildebrand B51
Class #:15821
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.
Spring 2024
#15820
COMPSCI 189 101 - DIS 101 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 16, 2024 - May 03, 2024
Tu
10:00 am - 10:59 am
Soda 405
Class #:15820
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 2024
#15819
COMPSCI 189 001 - LEC 001 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jonathan Shewchuk
Jan 16, 2024 - May 03, 2024
Mo, We
06:30 pm - 07:59 pm
Wheeler 150
Class #:15819
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

No Open 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.
2024 Fall
#29478
COMPSCI 189 107 - DIS 107 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
01:00 pm - 01:59 pm
Cory 540AB
Class #:29478
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.
2024 Fall
#29477
COMPSCI 189 106 - DIS 106 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
12:00 pm - 12:59 pm
Dwinelle 243
Class #:29477
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.
2024 Fall
#29476
COMPSCI 189 105 - DIS 105 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
11:00 am - 11:59 am
Cory 540AB
Class #:29476
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.
2024 Fall
#29475
COMPSCI 189 104 - DIS 104 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
10:00 am - 10:59 am
Wheeler 220
Class #:29475
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.
2024 Fall
#29474
COMPSCI 189 103 - DIS 103 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
Th
09:00 am - 09:59 am
Wheeler 202
Class #:29474
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.
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