Modeling, Learning, and Decision-Making

Spring 2022
#32562
COMPSCI 189 23DS - DIS 23DS offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Th
02:00 pm - 02:59 pm
Soda 320
Class #:32562
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 23DS 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
#32561
COMPSCI 189 22DS - DIS 22DS offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
12:00 pm - 12:59 pm
Soda 320
Class #:32561
Units:4

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

COMPSCI 189 - DIS 22DS 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
#32560
COMPSCI 189 21DS - DIS 21DS offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
Tu
11:00 am - 11:59 am
Soda 320
Class #:32560
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
COMPSCI 189 - DIS 21DS 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
#32548
COMPSCI 189 99DS - DIS 99DS offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 18, 2022 - May 06, 2022
12:00 am
Class #:32548
Units:4

Instruction Mode: In-Person Instruction

Open Seats

39 Unreserved Seats

COMPSCI 189 - DIS 99DS 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
#32547
COMPSCI 189 002 - LEC 002 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Marvin M Zhang
Jan 18, 2022 - May 06, 2022
Mo, We, Fr
01:00 pm - 01:59 pm
Evans 10
Class #:32547
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

39 Unreserved Seats

COMPSCI 189 - LEC 002 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
#27672
STAT 154 102 - LAB 102 offered through Statistics

Modern Statistical Prediction and Machine Learning

Austin Zane
Jan 18, 2022 - May 06, 2022
Mo
03:00 pm - 04:59 pm
Wheeler 30
Class #:27672
Units:4

Instruction Mode: In-Person Instruction

Open Seats

2 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.
Spring 2022
#26275
STAT 154 101 - LAB 101 offered through Statistics

Modern Statistical Prediction and Machine Learning

Austin Zane
Jan 18, 2022 - May 06, 2022
Mo
09:00 am - 10:59 am
Wheeler 120
Class #:26275
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.
Spring 2022
#26274
STAT 154 001 - LEC 001 offered through Statistics

Modern Statistical Prediction and Machine Learning

Nusrat Rabbee
Jan 18, 2022 - May 06, 2022
Tu, Th
05:00 pm - 06:29 pm
Etcheverry 3108
Class #:26274
Units:4

Instruction Mode: In-Person Instruction

Open Seats

15 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.
Spring 2022
#29169
STAT C102 999D - DIS 999D offered through Statistics

Data, Inference, and Decisions

Class #:29169
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - DIS 999D 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
#29168
STAT C102 999L - LAB 999L offered through Statistics

Data, Inference, and Decisions

Class #:29168
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.
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