INDENG242B
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INDENG 242B - Machine Learning and Data Analytics II
Subject
INDENG
Course Number
242B
Course Level
Graduate
Course Title
Machine Learning and Data Analytics II
Course Description
Following IEOR 142A/242A, this course further introduces students to essential
methodologies and recent trends in machine learning and data analytics. The
course will bridge theoretical foundations with applied data analytics by using
examples and real datasets from domains such as e-commerce, social media, finance,
and more. Students will gain experience with various data analytics packages in
Python and will deliver a comprehensive team project. Topics include: deep
learning, time series and survival analysis, end-to-end learning, causal inference,
reinforcement learning, and ethics, fairness and safety in artificial intelligence.
methodologies and recent trends in machine learning and data analytics. The
course will bridge theoretical foundations with applied data analytics by using
examples and real datasets from domains such as e-commerce, social media, finance,
and more. Students will gain experience with various data analytics packages in
Python and will deliver a comprehensive team project. Topics include: deep
learning, time series and survival analysis, end-to-end learning, causal inference,
reinforcement learning, and ethics, fairness and safety in artificial intelligence.
Minimum
4
Maximum
4
Grading Basis
Default Letter Grade; S/U Option
Prerequisites
IndEng 142A or IndEng 242A or equivalent introductory machine learning class.
Familiarity with the Python programming language.
Familiarity with the Python programming language.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.
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Credit Restrictions.
Students will receive no credit for IND ENG 242B after completing IND ENG 142B.
Credit Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.
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Formats
Lecture, Discussion
Term
Fall and Spring
Duration (in weeks)
15
Minimum Hours
3
Maximum Hours
3
Lecture Mode of Instruction
In Person
Minimum Hours
1
Maximum Hours
1
Discussion Mode of Instruction
In Person
Minimum Hours
8
Maximum Hours
8