INDENG142A

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INDENG 142A - Introduction to Machine Learning and Data Analytics

Industrial Engineering and Operations Research Undergraduate COE - College of Engineering

Subject

INDENG

Course Number

142A

Department

Industrial Engineering and Operations Research

Course Level

Undergraduate

Course Title

Introduction to Machine Learning and Data Analytics

Course Description

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, finance, the Internet, and more. Through these examples, conceptual exercises, data analysis exercises in Python, 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, time series modeling, clustering, principal component analysis, regularization, and large-scale learning with neural networks.

Minimum Units

4

Maximum Units

4

Grading Basis

Default Letter Grade; P/NP Option

Method of Assessment

Written Exam

Instructors

Grigas, Paul

Prerequisites

IND ENG 165 and IND ENG 172 or equivalent courses in probability and statistics. Prior exposure to optimization (either IND ENG 160 or IND ENG 162 or equivalent). Some programming experience/literacy is expected.

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.

-

Credit Restrictions. Upon passing, students can use the following course(s) to replace a deficient grade for this course.

Students will receive no credit for IND ENG 142A after completing IND ENG 142, IND ENG 242, IND ENG 242A, COMPSCI 189, COMPSCI 289, or STAT 154.

Credit Replacement Courses

-

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