ENGR 2011/2013: Engineering Analysis and Applications

Syllabus

Contact Information:

Lecture MWF: 2:00 - 2:50 PM (ENGR 304)
Laboratory M: 3:00 - 4:50 PM (ENGR 305)
Lecturer Joseph Picone, Professor
Office: ENGR 718
Office Hours: (MWF) 09:00 AM - 11:00 AM, 12:00 PM - 2:00 PM
Phone: 215-204-4841 (desk); 215-954-7076 (cell - preferred)
Email: picone@temple.edu
Teaching Assistants: Md Waqeeb Tahmeed Sayeed Chowdhury, PhD Student
Office: ENGR 603
Office Hours: (F) 3:30 PM - 5:30 PM (online via Zoom)
Phone: 215-433-2924 (Zoom, email or text preferred in that order)
Email: waqeebsayeed@temple.edu

Mirza Asif Haider, PhD Student
Office: ENGR 603
Office Hours: (W) 1:00 PM - 3:00 PM (online via Zoom)
Phone: 276-876-9809 (Zoom, email or text preferred in that order)
Email: mirza.asif.haider@temple.edu

Jannatoul Ferdous, MS Student
Office: ENGR 723B
Office Hours: (R) 3:00 PM - 5:00 PM (in-person or online via Zoom)
Phone: 267-934-0963 (Zoom, text or email)
Email: jannatul.ferdous0003@temple.edu
Grader(s) Abdulrahman Alshehri, BS EE Student
Office: Online via Zoom
Office Hours: By Appointment
Email: a.shehri@temple.edu
Email Help: temple_engineering_engr2011_help@googlegroups.com
Communication: temple_engineering_engr2011@googlegroups.com
Website http://www.isip.piconepress.com/courses/temple/engr_2011
Textbook W. Keith Nicholson
Linear Algebra with Applications
McGraw Hill Higher Education, 2009, ISBN 978-0070985100, 544 pages
URL: Download
References
and
Resources 		Alternate Textbook:
G. Strang
Introduction to Linear Algebra
Wellesley-Cambridge Press, 2016, ISBN 978-0-9802327-7-6, 574 pages
URL: https://math.mit.edu/~gs/linearalgebra/

K. Kuttler
A First Course in Linear Algebra
CreateSpace Independent Publishing Platform, 2017, ISBN 978-1542895521, 604 pages
URL: A First Course in Linear Algebra

Learning Python:
Wes McKinney
Python for Data Analysis
O'Reilly Media; First Edition
July 2013, 550 pages
URL: Python for Data Analysis

LearnPython.org: many excellent interactive tutorials.

Learning how to use the Internet to problem solve is another very important skill you will learn in this course. We often describe this as "learning how to learn." An amazing resource that contains an answer to just about any computer question you can imagine is Stack Overflow, where you can find answers to almost any programming question.
Prerequisites MATH 1042: Calculus II (Minimum Grade of C)

Course Description: Please see the university bulletin for a description of the course.

ABET Syllabus:: Please click here to view the ABET syllabus for this course.

University Policy Statements:: Please refer to the College of Engineering Policies and Procedures web site regarding the policies and procedures you are responsible for. The source location for this information is in the TU Portal: College of Engineering -> Advising -> Temple University Syllabus Policy.

Course Overview:

Linear algebra is the foundation upon which most modern engineering is built. Fields as diverse as electronics, controls and machine learning depend on it. In this course, we will learn how to write and manipulate mathematics using vectors and matrices, and we will also learn how to do these computations in a symbolic programming language, Python.

We will have three in-class exams in this course and a comprehensive final. Exams and quizzes will be theoretical and worked without the use of computers (unless indicated otherwise). Homework, which will be assigned weekly, will involve a mixture of analytic and computational work.

Quizzes will be given in class to encourage you to attend lecture classes and keep up with the daily work. You must be present in class to receive credit for the quizzes. You cannot participate in the quiz remotely if you miss class with an unexcused absence. If you miss a quiz without a prior excuse from the instructor, you receive a zero for that quiz with no exception. Make-up quizzes will not be given. The same policy applies to in-class exams and the final exam as well.

In-class exams will be submitted via written solutions. Homework assignments, quizzes, lab reports and exams are submitted by placing files in the following directories on the class server:

      /data/courses/engr_2011/current/homework/hw_xx/lastname_firstname
      /data/courses/engr_2011/current/quizzes/qu_xx/lastname_firstname
      /data/courses/engr_2011/current/labs/lab_xx/lastname_firstname
      /data/courses/engr_2011/current/exams/ex_xx/lastname_firstname

to prepare your solutions. Generate a compressed pdf so that your file sizes are less than 1 Mbyte. If you are not familiar with this feature of Adobe Acrobat, a tutorial can be found here.

Grading Policies:

Students will receive separate grades for the lecture and lab components of the course. The lecture component rubric is:

Item
Weight
Exam No. 1 10%
Exam No. 2 10%
Exam No. 3 10%
Final Exam 20%
Homework Assignments 25%
Quizzes 25%
TOTAL: 100%


The rubric for the laboratory component of the course is:

Item
Weight
Labs 1-13 80%
Lab Final 20%
TOTAL: 100%


Lecture Schedule:

The lecture component of ENGR 2011 meets three times a week and will cover the following topics:

Class
Date
Topic(s)
Online Materials
01
08/28
   Introduction to Programming in Python in Linux (Notes)  
slides | video | code
02
08/30
   File I/O and Loops: Moving Data In and Out of Programs (Notes)  
slides | video | code
03
09/01
   Vector and Matrix Representations (Notes)  
slides | video | code
04
09/06
   Systems of Equations, Elementary Operations, Gaussian Elimination and Rank (Sects. 1.1, 1.2)  
slides | video | code
05
09/08
   Homogeneous Equations and Applications to Circuit Analysis (Sects. 1.3, 1.5)  
slides | video | code
06
09/11
   Simple Vector and Matrix Algebra (Sects. 2.1, 2.2 - through Theorem 2.2.6)  
slides | video | code
07
09/13
   Transformations, Matrix Multiplication and Computer Graphics (Sects. 2.2, 2.3)  
slides | video | code
08
09/15
   Matrix Inversion (Sect. 2.4 - through Theorem 2.4.3))  
slides | video | code
09
09/18
   Properties of Inverses and Elementary Matrices (Sects. 2.4, 2.5)  
slides | video | code
10
09/20
   Linear Transformations (Sect. 2.6)  
slides | video | code
11
09/22
   Review: Exam No. 1  
slides | video | code
12
09/25
   LU Factorizations (Sect. 2.7)  
slides | video | code
13
09/27
   Exam No. 1: Lectures 01-11  
exams
14
09/29
   Markov Chains (Sect. 2.9)  
slides | video | code
15
10/02
   The Cofactor Expansion (Sect. 3.1)  
slides | video | code
16
10/04
   Determinants and Matrix Inverses (Sect. 3.2)  
slides | video | code
17
10/06
   Eigenvalues and Eigenvectors (Sect. 3.3)  
slides | video | code
18
10/09
   Diagonalization (Sect. 3.3)  
slides | video | code
19
10/11
   Applications to Systems of Differential Equations (Sects. 3.3 - 3.6)  
slides | video | code
20
10/16
   Vectors and Lines (Sect. 4.1)  
slides | video | code
21
10/18
   Planes and Projections (Sect. 4.2)  
slides | video | code
22
10/20
   More Cross Products and Linear Transformations (Sects. 4.3, 4.4)  
slides | video | code
23
10/23
   Computer Graphics Applications (Sect. 4.5)  
slides | video | code
24
10/25
   Review: Exam No. 2  
slides | video | code
25
10/27
   Vector Spaces (Sects. 5.1 - 5.3)  
slides | video | code
26
10/30
   Exam No. 2: Lectures 12-24  
exams
27
11/01
   Rank, Similarity and Diagonalization (Sects. 5.4, 5.5)  
slides | video | code
28
11/03
   Best Approximation and Least Squares Analysis (Sect. 5.6)  
slides | video | code
29
11/06
   Correlation and Variance (Sect. 5.7)  
slides | video | code
30
11/08
   Vector Spaces (Chapter 6 - Selected Topics)  
slides | video | code
31
11/10
   Linear Transformations and Composition (Chapter 7 - Selected Topics)  
slides | video | code
32
11/13
   Orthogonality and Positive Definite Matrices (Sects. 8.1-8.3)  
slides | video | code
33
11/15
   QR-Factorization, Eigenvalue Computations and Singular Value Decomposition (Part I), (Sects. 8.4, 8.5, 8.6.1)  
slides | video | code
34
11/17
   The Pseudoinverse of a Matrix, Complex Matrices (Sects. 8.6.4, 8.7)  
slides | video | code
35
11/27
   Inner and Outer Products (Sects. 10.1, 10.2)  
slides | video | code
36
11/29
   State Variables and the State Equations (Notes)  
slides | video | code
37
12/01
   Principal Components Analysis Using Eigenvalue and Eigenvector Analysis (Notes)  
slides | video | code
38
12/04
   Review: Exam No. 3  
slides | video | code
39
12/06
   Feature Importance and Analysis of Variance (Notes)  
slides | video | code
40
12/08
   Exam No. 3: Lectures 25-38  
exams
41
12/11
   Introduction to Neural Networks and Machine Learning (Notes)  
slides | video | code
42
12/18
   Final Exam (1:00 PM - 3:00 PM): Lectures 01-41 (Chapters 1-8)  
exams


Note that all previous lectures for this course are available online in the lectures archive and videos archive so there is no shortage of material available that covers the specific concepts discussed in this course.

Please also note that the dates above are fixed since they have been arranged to optimize a number of constraints.

Homework:

The homework schedule is as follows:

HW
Due Date
Item(s)
01
09/05
   Simple Linear Algebra  
02
09/11
   Solving Systems of Linear Equations  
03
09/18
   Matrix Algebra and Inverses  
04
09/25
   Matrix Inversion and Elementary Matrices  
05
10/02
   Linear Transformations and LU Factorization  
06
10/09
   Determinants and Matrix Inverses  
07
10/16
   Eigenvalues, Eigenvectors and Linear Dynamical Systems  
08
10/23
   System of Differential Equations, Vectors and Lines  
09
10/30
   Crossproducts, Linear Transformations and Computer Graphics  
10
11/06
   Linear Independence and Orthogonality  
11
11/13
   Rank, Diagonalization and Least Squares Approximations  
12
11/27
   Vector Spaces and Linear Transformations  
13
12/04
   Orthogonality, Eigenvalues and Complex Matrices  
14
12/11
   Inner Products and Norms  

Homework is due by 2:00 PM on the date shown. Late homework will not be accepted. Keeping up with the homework in this course is critical to your ability to absorb this material in a meaningful fashion.

Laboratories:

The laboratory component of ENGR 2011, which is designated as ENGR 2013, meets once a week and will cover the following topics:

Lab
Due Date
Item(s)
01
09/05
   Linux and Python Infrastructure  
02
09/11
   Who Wants To Be A Billionaire?  
03
09/18
   Who Says Gamers Don’t Know Math?  
04
09/25
   Can You Discover Hidden Structure in a Signal?  
05
10/02
   How Do You Find a Signal in Noise?  
06
10/09
   How Do I Analyze Circuits Using Linear Algebra?  
07
10/16
   Who Wants to Be a Billionaire? – Part II  
08
10/23
   Can We Model Physical Systems Using a System of Differential Equations?  
09
10/30
   How Can We Rotate Objects in 3D?  
10
11/06
   Who Wants to Be a Billionaire? – Part III  
11
11/13
   How Can We Characterize and Remove Noise in a Signal?  
12
11/27
   How Can We Optimally Compress Data and Discover Underlying Relationships?  
13
12/04
   What Do Eigenvalues Tell Us About Data?  
14
12/11
   Laboratory Final: Application Programming  

Students will submit their lab assignments online following the instructions provided in each lab.

Plagiarism Policy:

In this class, you are encouraged to collaborate with your classmates. Working as teams to learn how to solve engineering problems is a very important and efficient way to learn. As professionals, we rely on our vast network of colleagues to solve problems, learn new things, make key design decisions, etc. We all use the Internet quite a bit as you will soon see. However, the work you turn in must be original.

The first time you are caught will result in a grade of zero on the assignment. The second time you are caught will result in dismissal from the course with a failing grade.

Working in groups is really critical to your success in this course. There is a famous quote about how you can judge a person by examining their five closest friends. Get to know your classmates and arrange meeting times where you can work together. If you need help during these sessions, don't be afraid to dial up your instruction team. If we are around and available, we will be happy to jump in a Zoom session and answer questions. We are always up for a 2 AM debugging session ;)