|Session||Date||Lecture Module||Project Module|
|1||9/1||010: Introduction Basics
020: Project: Getting Started
030: Installation Instructions
|P01: Project Introduction|
|2||9/8||100A and 100B: Prediction and Linear Regression Part I and II||P02: Team Formation 1|
|3||9/15||110: NumPy A, Arrays
120: Pandas A
|P03: Team Formation 2|
|4||9/22||130A: Visualization A
140: Logistic Regression
|P04: Low Tech Demo|
|5||9/29||160: Predictive Classification, Titanic Example||P05: Starting Agile Implementation|
|6||10/6||170: ML Overview
180: Cross-Validation and Regularization
|P06: Minimum Viable Demo|
Any module from '03: Data Handling' to aid the project
|P07: Project Progress 1|
|8||10/20||410: Intro to Tensor FlowOption||P08: Project Progress 2|
|9||10/27||500: NLP Text Processing||P09: Project Progress 3|
|10||11/3||Track Option: 420: Neural Networks or 510: NLP Feature Engineering & Text Representation||P10: Project Progress 4|
|11||11/10||Track Option: 430: CNN or 520: NLP Learning Models||P11: Final Demo Preparation|
|12||11/17||Options for an elective module||P12: Final Demo Preparation|
This course is designed primarily for upper-level undergraduate engineering and technical students. Graduate students at a mezzanine level can also take a co-located section of the course. The course material offers an understanding at the intersection of foundational math mathematical concepts and current computer science tools, with applications of real-world problems. Math concepts include filtering, prediction, classification, decision-making, entropy as part of information theory, LTI systems, spectral analysis, and frameworks for learning from data. Computer science tools for this course include open source tools such as Python with Numpy, Pandas, NLTK, Tensor Flow, Flask, SPARK, YOLO, and other libraries. The course includes a team-based data application project.
One goal is that students who understand math concepts can bring them to life with scalable CS tools. And, students who are comfortable with computer software code can create systems by understanding selected, structured mathematical frameworks. This course is designed to be more applied than a traditional ML algorithms course as it includes a systems view and covers implementation concepts.
Applications of this course are broad. They include industry sectors such as finance, health, engineering, transportation, energy, and many others. The lab section of the course meets in parallel with the lecture. In the lab, the first 4 weeks are used to generate a story and low-tech demo for a real-world project that performs actions on data, and the following 8 weeks will be an agile sprint, with a demonstration of working project code by the end of the class. The skillset learned in this class can be applied to a broad range of industry sectors such as finance, health, engineering, transportation, energy, and many others.
TEXTS AND REQUIRED SUPPLIES
- General Information
- Github for Code and Slides
- Anaconda Python Environment on a personal computer
- Text Book:
- Innovation Engineering, the book – Required
- Hands-On Machine Learning with Scikit-Learn and TensorFlow By Aurélien Géron (optional, but highly recommended)
HOMEWORK, GRADING & ATTENDANCE
Class attendance and participation are expected, and sign-ins for sessions are tracked. Absences for unavoidable reasons should be pre-approved whenever possible via an email to the GSI
Grading: (Required to be taken on Letter Grade only)
The class will be graded according to the categories below. At the end of the class, there will be a poster presentation + live demo during the reading week where invited judges will provide an assessment of each project.
- Homework: 35%
- Quizzes: 15%
- Low Tech Validated Solution (Demo + MVP): 20%
- Final Project + Write up + Code Review: 30%
Based on our previous experience in the course, we have decided to use the following percentile thresholds for the final grading. We plan to award A (top 30%), A- (next 30%), B+ (next 25%), and case by case grading for the rest. We reserve the right to increase or decrease these thresholds based on the performance of the class.
Student Accommodations: Students with disabilities who need accommodations in order to have equal access to this course will be accommodated. If you have not done so already, please contact DSP and apply for services. If you are already eligible for services, please be sure to request your accommodation letters for this class. You are welcome to visit me in office hours or to schedule an individual appointment with me via email to review your accommodations.
- Monday 1-2pm in Etcheverry Hall Room 4176b
- Fridays 2-3pm in Etcheverry Hall, IEOR Conference Room
Arash Nourian: by appointment via email
Ikhlaq Sidhu: by appointment via Melissa Glass, email@example.com