2020 Fall
INDENG 290 005 - LEC 005
Special Topics in Industrial Engineering and Operation Research
Data-X
Arash Nourian
Aug 26, 2020 - Dec 11, 2020
Tu
02:00 pm - 04:59 pm
Internet/Online
Class #:33507
Units: 3
Instruction Mode:
Remote Instruction
Offered through
Industrial Engineering and Operations Research
Current Enrollment
Total Open Seats:
0
Enrolled:
Waitlisted:
Capacity:
Waitlist Max:
No Reserved Seats
Hours & Workload
2 to 3 hours of instructor presentation of course materials per week, and 4 to 6 hours of outside work hours per week.
Final Exam
TUE, DECEMBER 15TH
08:00 am - 11:00 am
Other classes by Arash Nourian
Course Catalog Description
Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research.
Class Description
This lecture will be recorded and available to students. Synchronous participation (online at the day/time listed) is required, however, some participation exceptions may be granted under limited circumstances.
Class Notes
**This course counts towards the Sutardja Certificate in Technology and Entrepreneurship. Additional information: https://scet.berkeley.edu/certificate-in-entrepreneurship-and-technology/**
3 units
Today, the world is literally reinventing itself with Data and AI. However,.. show more
3 units
Today, the world is literally reinventing itself with Data and AI. However,.. show more
**This course counts towards the Sutardja Certificate in Technology and Entrepreneurship. Additional information: https://scet.berkeley.edu/certificate-in-entrepreneurship-and-technology/**
3 units
Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ and being able to ‘make it work’ are not the same. And, in an area as important as Artificial Intelligence, Data Science, and Machine Learning; if we collectively cannot actually implement and create, then we reduce our competitive advantage, economic strength, and even national/global security.
The Data-X framework is designed to bridge the gap between theory and practice as well as academia and industry, by exposing students to state-of-the-art implementation techniques and mindsets.
Data-X frameworks, materials, code samples are all open source and available at the public site: https://data-x.blog/
This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building data science, AI, and Machine Learning applications and systems. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python libraries like NumPy for math array functions, Pandas for manipulation of tables, Scikit-learn for machine learning modeling, Tensorflow and Keras for deep learning, and many other topics related to NLP, Neural Networks, Recommender Systems etc.
The course also features an open-ended project that the students will work on in groups of five over the semester. The project topic is up for the students to choose. To see examples of projects created by earlier student groups you can visit: https://data-x.blog/projects/ show less
3 units
Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ and being able to ‘make it work’ are not the same. And, in an area as important as Artificial Intelligence, Data Science, and Machine Learning; if we collectively cannot actually implement and create, then we reduce our competitive advantage, economic strength, and even national/global security.
The Data-X framework is designed to bridge the gap between theory and practice as well as academia and industry, by exposing students to state-of-the-art implementation techniques and mindsets.
Data-X frameworks, materials, code samples are all open source and available at the public site: https://data-x.blog/
This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building data science, AI, and Machine Learning applications and systems. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python libraries like NumPy for math array functions, Pandas for manipulation of tables, Scikit-learn for machine learning modeling, Tensorflow and Keras for deep learning, and many other topics related to NLP, Neural Networks, Recommender Systems etc.
The course also features an open-ended project that the students will work on in groups of five over the semester. The project topic is up for the students to choose. To see examples of projects created by earlier student groups you can visit: https://data-x.blog/projects/ show less
Rules & Requirements
Repeat Rules
Reserved Seats
Current Enrollment
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials
Associated Sections
None