Modeling, Learning, and Decision-Making

Spring 2022
#30403
COMPSCI 182 999 - DIS 999 offered through Electrical Engineering and Computer Sciences

Designing, Visualizing and Understanding Deep Neural Networks

Jan 18, 2022 - May 06, 2022
12:00 am
Class #:30403
Units:4

Instruction Mode: In-Person Instruction

Time Conflict Enrollment Allowed

Open Seats

5 Unreserved Seats

COMPSCI 182 - DIS 999 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.
2021 Fall
#27705
COMPSCI 189 108 - DIS 108 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 25, 2021 - Dec 10, 2021
We
03:00 pm - 03:59 pm
Wheeler 120
Class #:27705
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
COMPSCI 189 - DIS 108 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.
2021 Fall
#31246
INDENG 142 103 - DIS 103 offered through Industrial Engineering and Operations Research

Introduction to Machine Learning and Data Analytics

Heyuan Liu, Anna Deza, Xinyu Li, Siddharth Kumaran
Aug 25, 2021 - Dec 10, 2021
Fr
04:00 pm - 04:59 pm
Internet/Online
Class #:31246
Units:3

Instruction Mode: Pending Review

No Open Seats
INDENG 142 - DIS 103 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.
2021 Fall
#28183
INDENG 142 001 - LEC 001 offered through Industrial Engineering and Operations Research

Introduction to Machine Learning and Data Analytics

Paul Grigas
Aug 25, 2021 - Dec 10, 2021
Tu, Th
03:30 pm - 04:59 pm
Internet/Online
Class #:28183
Units:3

Instruction Mode: Pending Review

Time Conflict Enrollment Allowed

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.
2021 Fall
#23305
STAT 154 102 - LAB 102 offered through Statistics

Modern Statistical Prediction and Machine Learning

Ryan C Theisen
Aug 25, 2021 - Dec 10, 2021
Mo
02:00 pm - 03:59 pm
Wheeler 220
Class #:23305
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.
2021 Fall
#23304
STAT 154 101 - LAB 101 offered through Statistics

Modern Statistical Prediction and Machine Learning

Ryan C Theisen
Aug 25, 2021 - Dec 10, 2021
Mo
10:00 am - 11:59 am
Moffitt Library 103
Class #:23304
Units:4

Instruction Mode: In-Person Instruction

Open Seats

17 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.
2021 Fall
#23303
STAT 154 001 - LEC 001 offered through Statistics

Modern Statistical Prediction and Machine Learning

Song Mei
Aug 25, 2021 - Dec 10, 2021
Tu, Th
05:00 pm - 06:29 pm
Moffitt Library 101
Class #:23303
Units:4

Instruction Mode: In-Person Instruction

Open Seats

26 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.
2021 Fall
#32489
STAT C102 999L - LAB 999L offered through Statistics

Data, Inference, and Decisions

Class #:32489
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.
2021 Fall
#32488
STAT C102 999 - DIS 999 offered through Statistics

Data, Inference, and Decisions

Class #:32488
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.
2021 Fall
#26477
STAT C102 108L - LAB 108L offered through Statistics

Data, Inference, and Decisions

Aug 25, 2021 - Dec 10, 2021
Mo
03:00 pm - 03:59 pm
Dwinelle 229
Class #:26477
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.
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