Modeling, Learning, and Decision-Making

2022 Fall
#22919
STAT 154 102 - LAB 102 offered through Statistics

Modern Statistical Prediction and Machine Learning

Tiffany M Tang
Aug 24, 2022 - Dec 09, 2022
Mo
02:00 pm - 03:59 pm
Wheeler 220
Class #:22919
Units:4

Instruction Mode: In-Person Instruction

Open Seats

9 Unreserved Seats

STAT 154 - LAB 102 Modern Statistical Prediction and Machine Learning more detail
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
2022 Fall
#22918
STAT 154 101 - LAB 101 offered through Statistics

Modern Statistical Prediction and Machine Learning

Tiffany M Tang
Aug 24, 2022 - Dec 09, 2022
Mo
10:00 am - 11:59 am
Wheeler 220
Class #:22918
Units:4

Instruction Mode: In-Person Instruction

Open Seats

13 Unreserved Seats

STAT 154 - LAB 101 Modern Statistical Prediction and Machine Learning more detail
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
2022 Fall
#22917
STAT 154 001 - LEC 001 offered through Statistics

Modern Statistical Prediction and Machine Learning

James Bentley Brown
Aug 24, 2022 - Dec 09, 2022
Tu, Th
05:00 pm - 06:29 pm
Etcheverry 3106
Class #:22917
Units:4

Instruction Mode: In-Person Instruction

Open Seats

22 Unreserved Seats

STAT 154 - LEC 001 Modern Statistical Prediction and Machine Learning more detail
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
2022 Fall
#26210
STAT C102 999L - LAB 999L offered through Statistics

Data, Inference, and Decisions

Class #:26210
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT 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.
2022 Fall
#26209
STAT C102 999 - DIS 999 offered through Statistics

Data, Inference, and Decisions

Class #:26209
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT 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.
2022 Fall
#25157
STAT C102 108L - LAB 108L offered through Statistics

Data, Inference, and Decisions

Ruiqi Zhong
Aug 24, 2022 - Dec 09, 2022
Mo
04:00 pm - 04:59 pm
Etcheverry 3105
Class #:25157
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - LAB 108L 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.
2022 Fall
#25156
STAT C102 108 - DIS 108 offered through Statistics

Data, Inference, and Decisions

Aug 24, 2022 - Dec 09, 2022
We
04:00 pm - 04:59 pm
Wheeler 202
Class #:25156
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - DIS 108 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.
2022 Fall
#25155
STAT C102 107L - LAB 107L offered through Statistics

Data, Inference, and Decisions

Aidan Thomas McLoughlin
Aug 24, 2022 - Dec 09, 2022
Mo
03:00 pm - 03:59 pm
Etcheverry 3105
Class #:25155
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - LAB 107L 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.
2022 Fall
#25154
STAT C102 107 - DIS 107 offered through Statistics

Data, Inference, and Decisions

Aug 24, 2022 - Dec 09, 2022
We
03:00 pm - 03:59 pm
Etcheverry 3109
Class #:25154
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - DIS 107 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.
2022 Fall
#25153
STAT C102 106L - LAB 106L offered through Statistics

Data, Inference, and Decisions

Aidan Thomas McLoughlin
Aug 24, 2022 - Dec 09, 2022
Mo
02:00 pm - 02:59 pm
Etcheverry 3105
Class #:25153
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - LAB 106L 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.
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