CS6301: Machine Learning for Engineers and Scientists

3 Credit Course, ECSS 2.311, 2019

Spring 2019

#f03c15 This is a previous offering of this class. See the teaching page for current courses.

Course Overview

Class Hours: Mo/We 10:00–11:15am
Class Room: ECSS 2.311

Instructor: Gautam Kunapuli
Office: ECSS 2.717
Email: Gautam-dot-Kunapuli-@-utdallas-dot-edu
Office Hours: Wednesdays, 2:30pm-4:30pm; and by appointment

Teaching Assistant: Siwen Yan
Email: Siwen-dot-Yan-@-utdallas-dot-edu
Office Hours: Tuesdays and Fridays, 1:00pm-2:30pm at ECSS 2.104A1

Course Description

The main aim of the course is to provide Engineers and Scientists with a hands-on understanding of a broad variety of machine-learning algorithms. The course introduces several key as well as emerging/state-of-the-art machine learning alogrithms with a view toward practical usage and applications. The course aims to explore applications of machine learning in engineering, bioinformatics, economics, computer vision and many others as well as good machine learning practices in data pre-processing, modeling and evaluation.

Python Resources

The programming assignments will require coding in Python and will primarily use scikit-learn to explore various machine-learning algorithms. The following books may be useful as a quick introduction to Python:

The following books are also useful references if you want to learn Python from scratch:

Textbooks and Course Materials

There is no required textbook for this class. However, the following books are useful references for various topics we will cover in this course:

  • Pattern Recognition and Machine Learning by Christopher M. Bishop; this is a standard textbook and reference for introductory machine learning;
  • Machine Learning: a Probabilistic Perspective by Kevin Murphy; another excellent book and reference, especially for probabilistic graphical models.

The following books are available online, free for personal use. Supplemental reading material will be assigned from these sources as often as possible.

  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (available online)
  • Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville (available online) is an excellent introductory textbook for a wide-variety of deep learning methods and applications.

For even more online resources, see Awesome Machine Learning, Joseph Misiti’s curated list of Machine Learning books, frameworks, libraries and software.

Syllabus and Schedule

1Jan 14 (mo)Introduction & Linear RegressionBishop, Ch. 1 
 Jan 16 (we)Linear Regression (continued)Andrew Ng’s Lecture Notes, Part I;
Shalev-Shwartz & Ben-David, Ch. 9.2;
Kilian Weinberger’s Lecture Notes (probabilistic view)
2Jan 21 (mo)Martin Luther King Day
No class
 Jan 23 (we)PerceptronKilian Weinberger’s Lecture Notes 
3Jan 28 (mo)Perceptron (continued)Computational complexity of
GD vs. Stochastic GD
HW1 Out
 Jan 30 (we)Support Vector MachinesAndrew Ng’s Lecture Notes;
Bishop, Ch. 7
4Feb 4 (mo)Support Vector Machines (continued)  
 Feb 6 (we)Decision TreesMitchell, Ch. 3;
Kilian Weinberger’s Lecture Notes
5Feb 11 (mo)Decision Trees (continued) HW1 Due
HW2 Out
 Feb 13 (we)Nearest Neighbor MethodsDaumé III, Ch. 3 
6Feb 18 (mo)Good Machine Learning Practices
pre-processing, model selection,
cross validation, missing data, evaluation

Kotsiantis et al., 2006 
 Feb 20 (we)Good Machine Learning Practices (continued)  
7Feb 25 (mo)Naive BayesJerry Zhu’s Lecture Notes;
Mitchell 2nd ed. Ch. 3.1-3.2;
Daumé III, Ch. 9
HW2 Due
HW3 Out
 Feb 27 (we)Naive Bayes (continued);
Logistic Regression
Mitchell 2nd ed. Ch. 3.3-3.5;
Bishop, 8.4.1, 9.2, 9.3, 9.4;
Andrew Ng’s Lecture Notes, Pt II;
Kilian Weinberger’s Lecture Notes
8Mar 4 (mo)Logistic Regression (continued) Mid-Term Projects Begin
 Mar 6 (we)Ensemble Methods: BaggingBishop, Ch. 14;
Hastie et al., Ch. 7.1-7.6, 8.7;
Visualization of the Bias-Variance Tradeoff
9Mar 11 (mo)Ensemble Methods: BoostingHastie et al., Ch. 15;
Freund and Schapire, 1999
HW3 Due
 Mar 13 (we)Ensemble Methods: Gradient BoostingFriedman, 99;
Mason et al., 99;
Visualizing Gradient Boosting;
Tong He’s Presentation on XGBoost
10Mar 18 (mo)Spring Break
No class
 Mar 20 (we)Spring Break
No class
11Mar 25 (mo)ClusteringTan et al., Ch. 8 
 Mar 27 (we)Clustering (continued)
Principal Components Analysis
Andrew Ng’s Lecture Notes, Pt IIMid-Term Projects Due
HW4 Out
12Apr 1 (mo)Principal Component Analysis (continued)  
 Apr 3 (we)Neural NetworksGoodfellow et al., Ch. 6Final Project Teams, Proposal Due
13Apr 8 (mo)Neural Networks (continued)  
 Apr 10 (we)Convolutional Neural NetworksGoodfellow et al., Ch. 9HW4 Due
14Apr 15 (mo)Recurrent Neural NetworksGoodfellow et al., Ch. 10
Kishan Athrey’s Tutorial on Keras
 Apr 17 (we)Other Machine Learning Areas  
15Apr 22 (mo)Project Presentations
Attendance is mandatory
PresentationsPresentation Guidelines
 Apr 24 (we)Project Presentations
Attendance is mandatory
16Apr 29 (mo)Project Presentations
Attendance is mandatory
 May 1 (we)Project Presentations
Attendance is mandatory
PresentationsLast day of classes
xxMay 8 (we)Final Project Reports Due  

The topic schedule is subject to change at the instructor’s discretion. Please check this page regularly for lecture slides, additional references and reading materials.


The grading rubric is subject to change at the instructor’s discretion. Please check this page at the start of the semster.

  • 50%, Homework (4, each 12.5%)
  • 20%, Mid-term Project
  • 30%, Final Project

Course Policies

Attendance Policy

Classroom attendance for all lectures is mandatory. Prolonged absence from the lectures may lead to substantial grade penalties:

  • two consecutive absences, no penalty;
  • 3 consecutive absences: 1 letter grade drop;
  • 4 consecutive absences, F grade.

Absence due to emergency or extenuating circumstances can be excused, but proof may be required.

Homework Policy

Homework assignments are due at the start of class on the due date without exceptions, unless permission was obtained from the instructor in advance. Homework and assignment deadlines will not be extended except under extreme university-wide circumstances such as weather emergencies.

All homeworks, programming projects, take-home exams (if any) are to be written up and completed individually. You may discuss, collaborate, brainstorm and strategize ideas, concepts and problems with other students. However, all written solutions and coded programs must be your own. Copying another student’s work or allowing other students to copy your work is academically dishonest.

Academic Integrity

All students are responsible for adhering to UT Dallas Community Standards and Conduct, particularly regarding Academic Integrity and Academic Dishonesty. Any academic dishonesty, including, but not restricted to plagiarism (including from internet sources), collusion, cheating, fabrication, will result in a zero score on the assignment/project/exam and possible disciplinary action.

Students with Disabilities

UT Dallas is committed to equal access in all endeavors for students with disabilities. The Office of Student Accessability (OSA) provides academic accommodations for eligible students with a documented disability. Accommodations for each student are determined by OSA on an individual basis, with input from qualified professionals. Accommodations are intended to level the playing field for students with disabilities, while maintaining the academic integrity and standards set by the University. If you think you qualify for an academic accommodation, please visit OSA to determine eligibility.

If you have already received academic accommodation, please contact me by e-mail to schedule an appointment before classes start, if possible.