INDENG142B
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INDENG 142B - Machine Learning and Data Analytics II
Subject
INDENG
Course Number
142B
Course Level
Undergraduate
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 Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Alternative Final Assessment
Prerequisites
IndEng 142A or IndEng 242A or equivalent introductory machine learning class. Familiarity
with the Python programming language.
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 142B after completing IND ENG 242B.
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
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Lecture Hours Min
3
Lecture Hours Max
3
Lecture Mode of Instruction
In Person
Discussion Hours
1
Discussion Hours Min
1
Discussion Hours Max
1
Discussion Mode of Instruction
In Person
Outside Work Hours
8
Outside Work Hours Min
8
Outside Work Hours Max
8