Modeling, Learning, and Decision-Making

Spring 2022
#29167
STAT C102 001 - LEC 001 offered through Statistics

Data, Inference, and Decisions

Nika Haghtalab, Ramesh Sridharan
Jan 18, 2022 - May 06, 2022
Tu, Th
09:30 am - 10:59 am
Internet/Online
Class #:29167
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - LEC 001 Data, Inference, and Decisions more detail
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
Spring 2022
#29195
DATA C102 999L - LAB 999L offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Class #:29195
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C102 - LAB 999L Data, Inference, and Decisions more detail
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
Spring 2022
#29194
DATA C102 999 - DIS 999 offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Class #:29194
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C102 - DIS 999 Data, Inference, and Decisions more detail
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
Spring 2022
#29193
DATA C102 001 - LEC 001 offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Nika Haghtalab, Ramesh Sridharan
Jan 18, 2022 - May 06, 2022
Tu, Th
09:30 am - 10:59 am
Internet/Online
Class #:29193
Units:4

Instruction Mode: Online

Time Conflict Enrollment Allowed

No Open Seats
DATA C102 - LEC 001 Data, Inference, and Decisions more detail
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
Spring 2022
#22770
COMPSCI 189 118 - DIS 118 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
08:00 pm - 08:59 pm
Wheeler 200
Class #:22770
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 118 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
#22769
COMPSCI 189 117 - DIS 117 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
07:00 pm - 07:59 pm
Wheeler 200
Class #:22769
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 117 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
#22768
COMPSCI 189 105 - DIS 105 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
We
06:00 pm - 06:59 pm
Wheeler 130
Class #:22768
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 2022
#22520
COMPSCI 189 103 - DIS 103 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
We
04:00 pm - 04:59 pm
Hildebrand B51
Class #:22520
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 2022
#22519
COMPSCI 189 102 - DIS 102 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
We
03:00 pm - 03:59 pm
Etcheverry 3113
Class #:22519
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 2022
#22183
COMPSCI 189 999 - DIS 999 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
12:00 am
Class #:22183
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

45 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.
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