Modeling, Learning, and Decision-Making

2022 Fall
#25033
STAT C102 101 - DIS 101 offered through Statistics

Data, Inference, and Decisions

Aug 24, 2022 - Dec 09, 2022
We
09:00 am - 09:59 am
Etcheverry 3107
Class #:25033
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
STAT C102 - DIS 101 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
#24964
STAT C102 001 - LEC 001 offered through Statistics

Data, Inference, and Decisions

Jacob Noah Steinhardt, Ramesh Sridharan
Aug 24, 2022 - Dec 09, 2022
Fr
02:00 pm - 04:59 pm
Lewis 100
Class #:24964
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.
2022 Fall
#28884
INDENG 142 102 - DIS 102 offered through Industrial Engineering and Operations Research

Introduction to Machine Learning and Data Analytics

Umut Uygur, Hyungki Im, Bennett Cole Cohen
Aug 24, 2022 - Dec 09, 2022
We
03:00 pm - 03:59 pm
Barker 101
Class #:28884
Units:3

Instruction Mode: In-Person Instruction

No Open Seats
INDENG 142 - DIS 102 Introduction to Machine Learning and Data Analytics more detail
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning.
2022 Fall
#28883
INDENG 142 101 - DIS 101 offered through Industrial Engineering and Operations Research

Introduction to Machine Learning and Data Analytics

Umut Uygur, Hyungki Im, Bennett Cole Cohen
Aug 24, 2022 - Dec 09, 2022
We
04:00 pm - 04:59 pm
Cory 277
Class #:28883
Units:3

Instruction Mode: In-Person Instruction

Open Seats

3 Unreserved Seats

INDENG 142 - DIS 101 Introduction to Machine Learning and Data Analytics more detail
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning.
2022 Fall
#28099
INDENG 142 001 - LEC 001 offered through Industrial Engineering and Operations Research

Introduction to Machine Learning and Data Analytics

Paul Grigas, Bennett Cole Cohen
Aug 24, 2022 - Dec 09, 2022
Tu, Th
12:30 pm - 01:59 pm
Evans 10
Class #:28099
Units:3

Instruction Mode: In-Person Instruction

No Open Seats
INDENG 142 - LEC 001 Introduction to Machine Learning and Data Analytics more detail
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning.
2022 Fall
#26202
DATA C102 999L - LAB 999L offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Class #:26202
Units:4

Instruction Mode: In-Person Instruction

Open Seats

11 Unreserved 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.
2022 Fall
#26201
DATA C102 999 - DIS 999 offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Class #:26201
Units:4

Instruction Mode: In-Person Instruction

Open Seats

11 Unreserved 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.
2022 Fall
#25041
DATA C102 001 - LEC 001 offered through Data Science Undergraduate Studies

Data, Inference, and Decisions

Jacob Noah Steinhardt, Ramesh Sridharan
Aug 24, 2022 - Dec 09, 2022
Fr
02:00 pm - 04:59 pm
Lewis 100
Class #:25041
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

11 Unreserved 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.
2022 Fall
#31113
COMPSCI 182 113 - DIS 113 offered through Electrical Engineering and Computer Sciences

Designing, Visualizing and Understanding Deep Neural Networks

Aug 24, 2022 - Dec 09, 2022
Tu
04:00 pm - 04:59 pm
Soda 405
Class #:31113
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 182 - DIS 113 Designing, Visualizing and Understanding Deep Neural Networks more detail
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
2022 Fall
#31112
COMPSCI 182 112 - DIS 112 offered through Electrical Engineering and Computer Sciences

Designing, Visualizing and Understanding Deep Neural Networks

Aug 24, 2022 - Dec 09, 2022
We
04:00 pm - 04:59 pm
Evans 9
Class #:31112
Units:4

Instruction Mode: In-Person Instruction

Open Seats

36 Unreserved Seats

COMPSCI 182 - DIS 112 Designing, Visualizing and Understanding Deep Neural Networks more detail
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
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