EECS16A  Designing Information Devices and Systems I
Fall 2019
Schedule
The schedule is tentative and subject to change.(Please scroll horizontally if you're viewing this on your phone.)
Week  Date  Lecture Topic  Section  Lab  Homework 

0



Homework 0
Due 09/04 We (PDF) (Sols) (Self Grade) (Practice) 

08/29 Th 
Overview. Introduction to Imaging.
(Slides) (Webcast) (Note 0) 

1

09/03 Tu 
Systems of Linear Equations and Gaussian Elimination
(Slides) (Docucam Notes) (Ipy Demo) (Webcast) (Note 1A) (Note 1B) 
Anaconda setup + ipython bootcamp (Tu 9/3M 9/9)
(Presentation) (Bootcamp) 
Homework 1
Due 09/06 Fr (PDF) (iPy) (Sols) (iPy Sols) (Self Grade) (Practice) 

09/05 Th 
More Gaussian Elimination. Matrix Vector Multiplication
(Docucam Notes) (Webcast) (Note 2A) 
Section 1B
(Dis) (iPython) (Ans) 

2

09/10 Tu 
Introduction to Proofs. Span, Linear Dependence and Independence
(Docucam Notes) (Webcast) (Note 3) (Note 4) 
Section 2A
(Dis) (Ans) 
Imaging 1 (Tu 9/10M 9/16)
(Imaging Lab 1) (Presentation) 
Homework 2
Due 09/13 Fr (PDF) (iPy) (Sols) (iPy Sols) (Self Grade) (Practice) 
09/12 Th 
Linear Transformations, Matrixmatrix multiplication
(Docucam Notes) (Webcast) (Note 2B) (Note 5) 
Section 2B
(Dis) (iPython) (Ans) 

3

09/17 Tu 
Inversion
(Docucam Notes (*updated*)) (Webcast) (Note 6) 
Section 3A
(Dis) (Ans) 
Imaging 2 (Tu 9/17M 9/23)
(Imaging Lab 2) (Presentation) 
Homework 3
Due 09/20 Fr (PDF) (iPy) (Sols) (iPy Sols) (Self Grade) (Practice) 
09/19 Th 
Vector Spaces: Null spaces and Columnspaces
(Docucam Notes) (Webcast) (Note 7) 
Section 3B
(Dis) (Ans) 

4

09/24 Tu 
Page Rank, Eigenvalues, Eigenspaces
(Docucam Notes) (Webcast) (Determinant Demo) 
Section 4A
(Dis) (Ans) 
Buffer (Tu 9/24F 9/27) (Imaging 1, 2)

Homework 4
Due 09/27 Fr (PDF) (iPy) (Sols) (iPy Sols) (Self Grade) (Practice) 
09/26 Th 
Eigenvalues and Eigenspaces
(Docucam Notes) (Webcast) (Eigenvalue Demo) 
Section 4B
(Dis) (Ans) 

5

10/01 Tu 
More Eigenvalues and Eigenvectors
(Docucam Notes) (Webcast) 
Section 5A
(Dis) (Ans) 
Imaging 3 (M 9/30F 10/4)
(Imaging Lab 3) (Presentation) 
Homework 5
Due 10/04 Fr (PDF) (iPy) (Sols) (iPy Sols) (Self Grade) 
10/03 Th 
Intro to Circuit Analysis
(Docucam Notes) (NVA Notes) (Webcast) (Review Session) 
Section 5B
(Dis) (Ans) 

6
Midterm 10/7 8–10pm Blank exam Answers 
10/08 Tu 
Introduction to Modeling with Circuit Elements
(Docucam Notes) (Webcast) 
Section 6A
(Dis) (Ans) 
No Lab (M 10/7F 10/11)

Homework 6
Due 10/11 Fr (PDF) 
10/10 Th 
Power and Voltage/Current Measurement

Section 6B
(Dis) (Ans) 

7

10/15 Tu 
2D Touchscreen

Section 7A

Touch 1 (M 10/14F 10/18)

Homework 7
Due 10/18 Fr (PDF) 
10/17 Th 
Superposition and Equivalence

Section 7B


8

10/22 Tu 
Introduction to Capacitive Touchscreen

Section 8A

Touch 2 (M 10/21F 10/25)

Homework 8
Due 10/25 Fr 
10/24 Th 
Capacitance modeling and measurement

Section 8B


9

10/29 Tu 
Opamps and Negative Feedback

Section 9A

Touch 3A (M 10/28F 11/1)

Homework 9
Due 11/01 Fr 
10/31 Th 
Opamp Circuit Analysis

Section 9B


10
Midterm 11/4 8–10pm 
11/05 Tu 
Design Procedure and Design Examples

Section 10A

Buffer (Tu 11/5F 11/8) (Imaging 3, Touch 1, 2, 3A)

Homework 10
Due 11/08 Fr 
11/07 Th 
Design Examples

Section 10B


11

11/12 Tu 
Locationing for GPS; Trilaterations

Section 11A

Touch 3B (Tu 11/12M 11/18)

Homework 11
Due 11/15 Fr 
11/14 Th 
Similarity measures in data: Correlation measures

Section 11B


12

11/19 Tu 
Fitting data using Least Squares

Section 12A

APS 1 (Tu 11/19M 11/25)

Homework 12
Due 11/22 Fr 
11/21 Th 
Least Squares Cont.

Section 12B


13

11/26 Tu 
Greedy algorithms for machine learning

Section 13A

None

Homework 13
Due 12/02 Mo 
Thanksgiving (11/2711/29)


14

12/03 Tu 
Machine Learning continued

Section 14A

APS 2 (M 12/2F 12/6)

Homework 14
Due 12/06 Fr 
12/05 Th 
Machine Learning continued

Section 14B


15

12/10 Tu 
RRR Week

Section 15A

Buffer (Touch 3B, APS 1, APS 2)


12/12 Th 
RRR Week

Section 15B


Final 12/20 8–11am 
Final Examinations


Notes
Be aware that the unupdated notes may not reflect this semester's syllabus, and are subject to change. Note 0  Overview
 Note 1A  Systems of Linear Equations
 Note 1B  Gaussian Elimination
 Note 2A  Matrices and Vectors
 Note 2B  Matrix Multiplication
 Note 3  Linear Independence and Span
 Note 4  Mathematical Thinking and Derivation
 Note 5  Water Reservoirs, Pumps and Matrix Multiplication
 Note 6  Matrix Inversion
 Note 7  Vector Spaces
 Note 8  Matrix Subspaces
 Note 9  Eigenvalues and Eigenvectors
 Note 10  Change of Basis
 Note 11  Introduction to Circuit analysis
 Note 12  Voltage Dividers and Resistors
 Note 13  Resistive Touchscreen and Power
 Note 14  More Resistive Touchscreen
 Note 15  Superposition and Equivalence
 Note 16  Capacitors
 Note 17  Capacitive Touchscreen and OpAmps
 Note 18  OpAmps in Negative Feedback
 Note 19  More OpAmp Topologies
 Note 20  OpAmp Current Source and Circuit Design
 Note 21  Inner Products and GPS
 Note 22  Trilateration and Correlation
 Note 23  Least Squares
 Note 24  Orthogonal Matching Pursuit
Discussion Layout
Color  Recommended Students 

Red  Anyone 
Orange  Firstyears 
Green  Transfers 
Blue  Students who took Math 54 
Purple  Underrepresented Groups 
Magenta  Extended Section 
Note: Monday and Wednesday discussion sections cover different material, and you need to go to BOTH a Monday and Wednesday discussion each week. Scroll horizontally to view entire table.
Students are welcome to attend any discussion they choose. This year we are offering a variety of sections that may be of interest to different groups of students. Firstyears are encouraged to go to the sections colored in Orange, transfer students are encouraged to go to the sections colored Green, and students who have a prior background in linear algebra/have taken Math 54 are encouraged to go to the Blue sections. (We emphasize that the class does not rely on any prior linear algebra knowledge, and we do NOT expect you to have taken Math 54 or a linear algebra class in advance.)
This year, we are also trying out two new types of sections. The Magenta section will cover the same material from a one hour discussion over a twohour time slot, so if you prefer taking things slowly, you might want to try this section. Finally, we know that students in this class come from a wide variety of backgrounds. For this reason, we are offering the purple section  students who may feel underrepresented in the class are invited to attend this section if they choose. All this said, please try out various sections and choose the section that works best for you.
Resources
Piazza (Ask Questions Here)
Homework Practice Problems
Recommended Texts
 EE16A's Guide to the Recommended Texts
 ELECTRONICS Reader (50MB) by Ali M. Niknejad, or this smaller file without links (5MB)
 Intoduction to Linear Algebra by Gilbert Strang, 5th Ed.
 Schaum's Outlines of Linear Algebra, 5th ed. by Seymour Lipschutz and Marc Lipson. Free if login from the university network. Also see roaming passports.
 Schaum's Outline of Electric Circuits, 7th ed. by Mahmood Nahvi and Joseph A. Edminister. Free if login from the university network. Also see roaming passports.
Circuit Cookbooks
 Recipe: Nodal Analysis!
 Recipe: Charge Sharing!
 Recipe: Thevenin and Norton Equivalents! (INCOMPLETE)
 Recipe: Design Topologies!
Extra Resources
 StepByStep Gaussian Elimination by Andi Gu, a former student
 studEE16A (may need to load each page twice to view the LaTeX)
 Fun with Stacked Caps
Setting up HowTo's
Past Exams
Past exams vary in scope from semester to semester, and may include topics that are not in scope for the current semester or module. Inscope topics are posted on Piazza 12 weeks before the exam. Spring '15: MT1 (solution) MT2 (solution) Final (solution)
 Fall '15: MT1 (solution) MT2 (solution) Final (solution)
 Spring '16: MT1 (solution) MT2 (solution) Final (solution)
 Fall '16: MT1 (solution) MT2 (solution) Final (solution)
 Spring '17: MT1 (solution) MT2 (solution)
 Summer '17: MT1 (solution) MT2 (solution)
 Fall '17: MT1 (solution) MT2 (solution) Final solution
 Spring '18: MT1 (solution) MT2 Final
 Fall '18: MT1 (solution) MT2
 Spring '19: MT1 (solution) MT2 (solution)
Course Staff
Instructors
Please add berkeley.edu to the end of all emails
GSIs
Sarika Madhvapathy
Head
eecs16a@
Sam Weismann
Head
sam.weismann@
Jesse Conser
HW Management / Dis
eecs16a.hw@
Leyla Kabuli
Head Lab
lakabuli@
Michelle Mao
Admin / Dis
michelle.mao@
Grace Zhang
Admin
grace.zhang@
Alice Ye
Dis
alice.ye@
Anika Ramachandran
Dis
anikar@
Deepshika Dhanasekar
Dis
ddhanasekar@
Jack Zhang
Dis
jack.tiger.zhang@
Kareem Ahmad
Dis
kareemalgalaly@
Miyuki Weldon
Dis
m.weldon@
Ryan Tsang
Dis
r_tsang@
Christos Adamopoulos
Dis / Content
christos.ad@
Craig Schindler
Dis / Content
craig.schindler@
Nirmaan Shanker
Dis / Content
2020nshanker@
Panagiotis Zarkos
Dis / Content
panzarkos@
Ricky Liou
Dis / Content
rliou92@
Terry Chern
Dis / Content
terry_chern@
Moses Won
Dis / Software
moseswon@
Amanda Jackson
Lab
amandajackson@
Avanthika Ramesh
Lab
avanthika.ramesh@
David Deng
Lab
davezdeng8@
Jianshu Chi
Lab
jianshuchi@
Jodi Loo
Lab
jodi.loo@
Linda Du
Lab
ljdu@
Matt McPhail
Lab
matt.mcphail@
Peru Dayani
Lab
perudayani@
Sang A Park
Lab
sangapark@
Seiya Ono
Lab
scono12@
Michael Kellman
Content
kellman@
Neelesh Ramachandran
Content
neelesh.r@
Rahul Arya
Content
rahularya@
Sumer Kohli
Content
sumer.kohli@
Zachary GolanStrieb
Content
zacharyjgs@
Policies
Course Info
The EECS 16AB series (Designing Information Devices and Systems) is a pair of introductorylevel courses introducing students to EECS. The courses have a particular emphasis on how to build and understand systems interacting with the world from an informational point of view. Mathematical modeling is an important theme throughout these courses, and students will learn many conceptual tools along the way. These concepts are rooted in specific application domains. Students should understand why they are learning something.
An important part of being a successful engineer is being able to identify the important and relevant structure in a complex problem while ignoring minor issues. EECS 16A focuses on modeling as abstraction: how can we see the relevant underlying structure in a problem? It introduces the basics of linear modeling, largely from a "static" and deterministic point of view. EECS 16B deepens the understanding of linear modeling and introduces dynamics and control, along with additional applications. Finally, EECS 70, (which can be thought of as the third course in this sequence – except without any labs), introduces additional discrete structures for modeling problems, and brings in probability.
In EECS 16A in particular, we will use the application domains of imaging and tomography, smartphones and touchscreens, and GPS and localization to motivate and inspire. Along the way, we will learn the basics of linear algebra and, more importantly, the linearalgebraic way of looking at the world. The emphasis will be on modeling and using linear structures to solve problems; the class is not just focused on how to do computations. We will learn about linear circuits, not merely as a powerful and creative way to help connect the physical world to what we can process computationally, but also as an exemplar of linearity and as a vehicle for learning how to do design. Circuits also provide a concrete setting in which to learn the key concept of "equivalence" – an important aspect of abstraction. Our hope is that the concepts you learn in EECS 16A will help you as you tackle more advanced courses and will help form a solid conceptual framework that will help you learn throughout your career.
Grade Breakdown
Our objective is to help you become the best engineer you can be, and grades are not everything. The various components of the class: homework, labs and exams are designed explicitly with this in mind. Every challenge is a growth opportunity. You will have the opportunity to gain points in the course through completing your homework, attending labs as well as through the exams.
This course is not graded on a curve. We will set absolute thresholds for performance that will map to grade boundaries. We encourage you to discuss the course material with each other and teach each other new ideas and concepts that you learn. Teaching the material is one of the best ways to learn, so discussing course material with colleagues in the class is a winwin situation for everyone. Grades are not everything, far from it, but that said, here is the breakdown for grading for this class.
Participation  20 points 
Homework  35 points 
Labs  45 points 
Midterm 1  50 points 
Midterm 2  50 points 
Final  100 points 
Notice that you can get many points by being regular with your homework and the labs. In addition, there will be opportunities to get extra credit in the class. Our goal is to help you learn the material as best as possible!
Grading Scale
This course is not curved. We define the following grading scale (in percentages):
A+  [100+]  A  [93+)  A  [90+) 
B+  [84+)  B  [75+)  B  [68+) 
C+  [65+)  C  [62+)  C  [58+) 
D+  [57+)  D  [55+)  D  [53+) 
F  [0, 53) 
ContentCreation Extra Credit
We will also award extracredit points for students who create content and learning tools that benefit the entire class (e.g. a video demo of your lab, an illustrative pictorial explanation of a concept, a nice iPython demo, creating lecture slides to share with students, other creative content that engages with the course content). These can be posted to Piazza under the “student_content” folder and will be awarded extra credit at the discretion of a TA. The TA must endorse the content as high quality. If you have an idea of something you want to do and are wondering if it will count as extra credit please contact eecs16a@berkeley.edu.
Exam Clobber Policy
This course spans a fairly broad set of ideas and concepts within a short period of time, and hence sustained and consistent effort and investment are critical to your success in this class. Similarly, by far the most common operating mode we have observed in previous students who struggled and/or failed this class was attempting to do the bare minimum in general and then catch up/cram right before the exams.
In order to formally encourage all of you to maintain the sustained effort that we have observed to be critical to success, we will be adopting a new policy regarding exam clobbering, participation, and effort. Specifically, for students who (1) complete an optional midterm redo and (2) perform significantly better on the corresponding part of the final (linear algebra or circuits) than on the relevant midterm, we will provide the opportunity to clobber a midterm.
If you qualify for the clobber (i.e. (1) and (2)), you may replace your lowest midterm score with your scaled score on the final exam according to the formula below.
Replacement MT score (on scale of 100) = max [MT score, final exam score  15% (on scale of 100)] This essentially allows you to replace your midterm grade by a higher grade  we want to reward improved performance.
If you complete the optional midterm redo for both midterms, and are eligible for a clobber on both midterm, the clobber that helps your score more is applied (i.e. you may clobber either Midterm 1 or Midterm 2, but not both, and only if you complete both midterm redos). Please note that even though lecture attendance is not included (for logistical reasons) in the two criteria for clobbering eligibility, we do strongly encourage you to attend lecture in person.
Participation
Participation is worth a maximum of 20 points, and is measured by discussion attendance  to get full participation credit, you must attend 16 discussions throughout the course of the semester. Your grade will be prorated by the number of discussions you attend; i.e., if you attend 12 discussions, you will have 12/16 * 20 = 15 points in this category.
Homework Party
Homework parties are your chance to meet and interact with other students, while also having the chance to get help from GSIs, Tutors and Faculty. This is your chance to have a social experience as part of the class. We expect students to treat each other with respect during homework parties as well as during all other parts of the class – including interactions on Piazza, discussion and office hours. Remember that each of you is coming into a class with different experiences and backgrounds – use this as an opportunity to learn from one another.
Wednesdays 24PM, Thursdays 911AM, and Thursdays 24PM, HW Party will be held in Soda’s Wozniak Lounge or Cory 144MA. Check the course calendar for location. Attending homework party highly encouraged.
Students are expected to help each other out, and if desired, form adhoc "pickup" homework groups in the style of a pickup basketball game. We highly encourage students to attend homework party.
Association of Women in EE&CS (AWE) Office Hours
AWE Office Hours are cohosted by the Association of Women in EE&CS (AWE). These OHs will be staffed by female course staff members and AWE members but will otherwise function the same as regular OHs. Our hope is to offer an alternative OH environment and give you more autonomy to learn in whichever environment best suits your personal learning style. All students are welcome to attend.
Professor Ranade Roundtables
This year, you have the opportunity to sit down and snack with Professor Ranade! Professor Ranade will be hosting several roundtables on Thursdays for the next few weeks. This is a chance to meet your professor outside of a formal, classroom environment. Please stop by for informal conversation about 16A, Berkeley, faculty life, research, applications of 16A, or anything else. Sign up at this link (plus, there will be snacks)!
Homework Submission
Homeworks are due on Friday night at 11:59 PM. You need to turn in a .pdf file consisting of your writtenup solutions that also includes an attached pdf "printout" of your .ipynb code on Gradescope; you may use your phone camera or any pagescanning app in order to turn your written homework into a PDF, as long as your work is clear and legible. In addition, Gradescope has an option to associate pages of your work to each homework problem. You must select the relevant pages for every problem. Any homework submissions that are turned in without the code “printout” (or screenshot) attached will receive a zero on the coded ipython notebook portions of the homework. Any problems without pages selected will receive zero credit. If you have any questions about the format of a homework submission, please go to office hours or homework party.
You will have the opportunity to resubmit your homework after homework solutions are released to get makeup credit. See below for details.
Homework Grading – SelfGrading
The point of homework in this class is for you to learn the material. To help you in doing this each student will grade their own homework in addition to being graded by 16A readers. After the HW deadline, official solutions will be posted online. You will then be expected to read them and enter your own scores and comments for every part of every problem in the homework on a simple coarse scale:
Score  Reason 

0  Didn't attempt or very very wrong 
2  Got started and made some progress, but went off in the wrong direction or with no clear direction 
5  Right direction and got halfway there 
8  Mostly right but a minor thing missing or wrong 
10  100% correct 
Note: You must justify selfgrades of 2, 5, or 8 with a comment. Grades of 0 and 10 do not need to be justified. If you are really confused about how to grade a particular problem, you should post on Piazza. This is not supposed to be a stressful process.
We will hold extra office hours that will do HW runthroughs after the HW solutions have been released. These will be held on Monday, and we encourage you to attend them to ask questions about grading and clarify your understanding of the HW and solutions.
Your selfgrades will be due on the Monday following the homework deadline at 11:59 PM sharp. We will accept late selfgrades up to a week after the original homework deadline for half credit on the associated homework assignment. If you don't enter a proper grade by this deadline, you are giving yourself a zero on that assignment. Merely doing the homework is not enough, you must do the homework; turn it in on time; read the solutions; do the selfgrade; and turn it in on time. Unless all of these steps are done, you will get a zero for that assignment.
We will automatically drop the lowest homework score from your final grade calculation. This drop is meant for emergencies. If you use this drop halfway into the semester, and request another, we cannot help you. EE47D students will not have their lowest homework score dropped.
Just like we encourage you to use a study group for doing your homework, we strongly encourage you to have others help you in grading your assignments while you help grade theirs.
Course readers are going to be grading and sending you occasional comments. Because we have reader grades, we will catch any attempts at trying to inflate your own scores. This will be considered cheating and is definitely not worth the risk. Your own scores will be used in computing your final grade for the course, adjusted by taking into account reader scores so that everyone is fairly graded effectively on the same scale. For example, if we notice that you tend to give yourself 5s on questions where readers looking at your homeworks tend to give you 8s, we will apply an upward correction to adjust.
Reader grades will be released on Gradescope about one week after the homework deadline. Readers grade questions either on a “coarse” or “fine” scale for each homework part. Coarsely graded question parts are worth a single point and are based on effort. Finely graded question parts are worth a total of 10 points and are graded using the same selfgrading rubric above. Homework regrade requests are typically due on Gradescope within 72 hours of reader grades being released. If a regrade request is submitted for a part of a question on the homework, the grader reserves the right to regrade the entire homework and could potentially take points off.
If you have any questions, please ask on Piazza.
SelfGrade Walkthrough
Homework Resubmission
Again, the point of homework in this class is to help you learn. We understand that sometimes work from other classes, midterms or your personal life can come in the way of making a homework deadline. For this reason we will allow you to resubmit your homework for 70% credit. Homework resubmissions must be HANDWRITTEN. Homework resubmissions will be due along with the selfgrades, so they will be due by 11:59pm Monday night. If you choose to resubmit your homework, you must submit two sets of selfgrades, one for the first submission and one for the second submission. For the second submission do selfgrades as normal. We will apply the 70% correction.
What does 70% credit mean? Let us say you only were able to get halfway through a problem during the first submission. You submitted your homework on Friday, and while going through the solutions you figured out how to do the whole problem. Your selfgrade for your first submission would be a 5/10. However, you can resubmit the homework problem with a fully correct solution and receive 70% of the remaining points as extra points, i.e. (105) * 70/100 = 3.5 extra points, and so your score for the problem would go from 5 points to 8.5 points.
Homework Effort Policy
Because the point of homework in this class is to help you learn, not to punish you for making small mistakes, if your final score (after resubmission and any other corrections are applied) on any homework is above 8/10, your grade will automatically be bumped up to 100% (10/10). If your final score is less than 8/10, it will be scaled accordingly so that a 6/10 will result in 75% (7.5/10).
Lab and Discussion Section Policies
Ways to check attendance will be posted on Piazza on a later date.
Labs for this class are not open section, you must go to your assigned lab section. If you finish the lab early, we encourage you to help other groups debug their lab. This will help you learn the material better and contribute towards a better learning experience for everyone.
You should aim to get checkedoff by the end of your lab section. If you don’t finish in time, you have until the beginning of your next lab section to get checkedoff. While labs are not meant to be burdensome, they are an essential part of the course. We have the following strict grading policy for labs: If you complete all the labs, you will receive full lab credit. If you fail to complete one lab, you will receive 42/45 lab credit. If you miss two labs, you will receive half credit. If you miss three or more labs, you will get an F in the class.
Number of Missed Labs  What Happens? 
0  You get full lab credit  45/45 
1  You get almost full lab credit  42/45 
2  You get half lab credit  22/45 
3 or more  You Fail the class  final letter grade: F 
Some lab sections are “buffer labs.” “Buffer labs” are a several day period in which no new labs begin. During buffer lab sections, you may get checked off for only one lab that occurred during that lab module. No other labs can be checked off. You may attend any buffer lab held during a buffer week. The schedule for each buffer week will be different, more details on buffer lab sections will be announced on Piazza for every module.
Wires on lab breadboards must be planar. Lab staff will ask students to redo their circuits before debugging them if the wires are nonplanar. The definition of planar wires on a breadboard is shown below:
Planar  Nonplanar 
Please note the three black chips in the picture on the left. Professor Boser’s favorite vehicle has three wheels  he rides a tricycle!
You may go to any discussion section. Certain sections will prioritize certain groups of students (e.g. firstyear, transfer students, students with prior linear algebra / Math 54 background). Again, we emphasize that we DO NOT expect you to have taken a linear algebra class before EECS16A. All other students are allowed to remain in the section at the discretion of the discussion TA in charge of that section. We encourage you to go to the same discussion sections every week so that the TAs can get to know you personally.
Exam Policies
There are two midterms and one final. The midterms will be October 7th, 2019 from 8pm to 10pm and November 4th, 2019 from 8pm to 10pm. The final will be held on Friday, December 20, from 8am to 11am. Makeup exams will not be scheduled.
Please plan for exams at these times. In case of an emergency on exam day, please email the Head GSI at eecs16a@berkeley.edu as soon as possible and provide details of the issue as well as a contact phone number. Emergency exam conflicts will be handled on a casebycase basis. Exam conflicts originating from a lecture conflict will not be accommodated.
On exam day, you must bring your Cal student ID to your exam location. Locations and logistics will be posted on Piazza closer to the exam dates. If you do not take your exam in the correct location, a large penalty will be applied to your exam score. Additionally, regrade requests on Gradescope are typically due within a week of exams being released on Gradescope. Late regrade requests will not be considered. If a regrade request is submitted for a part of a question on the exam, the grader reserves the right to regrade the entire exam and could potentially take points off.
Exceptions and Exam Accommodation
Any requests for exceptions should be emailed to the Head GSI at eecs16a@berkeley.edu. Email the exception request out as soon as possible. Exceptions will be handled on a casebycase basis. Since there is one homework drop, missing homework is rarely excused. Examples of situations that merit an exception are medical emergencies and family emergencies. It will be easier for us to grant an exception if you have a doctor’s note or other documentation.
Any requests for exam accommodation should be emailed to the Head GSI at eecs16a@berkeley.edu within 2 weeks of the start of the semester. Exceptions will be handled on a casebycase basis. Examples of situations that merit an exception are medical emergencies and family emergencies. It will be easier for us to grant an exception if you have a doctor’s note or other documentation.
Course Communication
The instructors and TAs will post announcements, clarifications, hints, etc. on Piazza. Hence you must check the EECS16A Piazza page frequently throughout the term. (You should already have access to the EECS16A Fall 2019 forum. If you do not, please let us know.)
If you have a question, your best option is to post a message on Piazza. The staff (instructors and TAs) will check the forum regularly, and if you use the forum, other students will be able to help you too. When using the forum, please avoid offtopic discussions, and please do not post answers to homework questions before the homework is due. Also, always look for a convenient category to post the question to (for example, each homework will have its own category, so please post there). That will ensure you get the answer faster.
If your question is personal or not of interest to other students, you may mark your question as private on Piazza, so only the instructors will see it. If you wish to talk with one of us individually, you are welcome to come to our office hours. Please reserve email for the questions you can't get answered in office hours, in discussion sections, or through the forum.
For any exceptions that are of a personal nature, please contact the head GSI at eecs16a@berkeley.edu. Technical and homework questions are best resolved in homework and during office hours.
It can be challenging for the instructors to gauge how smoothly the class is going. We always welcome any feedback on what we could be doing better. If you would like to send anonymous comments or criticisms, please fill out this anonymous feedback form.
Collaboration
We encourage you to work on homework problems in study groups of two to four people; however, you must always write up the solutions on your own. Similarly, you may use books or online resources to help solve homework problems, but you must always credit all such sources in your write up, and you must never copy material verbatim. Using previous EECS16A homework, exam, and lab solutions is strictly prohibited, and will be considered academic dishonesty. This is not how you want to start your career as an engineer.
We expect that most students can distinguish between helping other students and cheating. Explaining the meaning of a question, discussing a way of approaching a solution, or collaboratively exploring how to solve a problem within your group is an interaction that we encourage strongly. But you should write your homework solution strictly by yourself so that your hands and eyes can help you internalize the subject matter. You should acknowledge everyone whom you have worked with, or who has given you any significant ideas about the homework. This is good scholarly conduct.
Don't Be Afraid to Ask for Help
Are you struggling? Please come talk with us! The earlier we learn about your struggles, the more likely it is that we can help you. Waiting until right before an exam or the last few weeks of the semester to let us know about your problems is not an effective strategy  the later it is, the less we will be able to help you.
Even if you are convinced that you are the only person in the class who is struggling, please overcome any feelings of embarrassment or guilt, and come ask for help as soon as you need it – we can almost guarantee you're not the only person who feels this way. Don't hesitate to ask us for help – we really do care that you thrive! You can email eecs16a@berkeley.edu, or email / talk to any TA at any time  we’re happy to help.
Advice
The following tips are offered based on our experience.
Do the homeworks! The homeworks are explicitly designed to help you to learn the material as you go along. There is usually a strong correlation between homework scores and final grades in the class.
Keep up with lectures! Discussion sections, labs and homeworks all touch on portions of what we discuss in lecture. Students do much better if they stay on track with the course. That will also help you keep the pace with your homework and study group.
Take part in discussion sections! Discussion sections are not auxiliary lectures. They are an opportunity for interactive learning. The success of a discussion section depends largely on the willingness of students to participate actively in it. As with office hours, the better prepared you are for the discussion, the more you are likely to benefit from it.
Please come to office hours! We love to talk to you and do a deep dive to help you understand the material better.
Form study groups! As stated above, you are encouraged to form small groups (two to four people) to work together on homeworks and on understanding the class material on a regular basis. In addition to being fun, this can save you a lot of time by generating ideas quickly and preventing you from getting hung up on some point or other. Of course, it is your responsibility to ensure that you contribute actively to the group; passive listening will likely not help you much. And recall the caveat above that you must write up your solutions on your own. We advise you strongly to spend some time on your own thinking about each problem before you meet with your study partners; this way, you will be in a position to compare ideas with your partners, and it will get you in practice for the exams. Make sure you work through all problems yourself, and that your final writeup is your own. Some groups try to split up the problems ("you do Problem 1, I'll do Problem 2, then we'll swap notes"); not only is this a punishable violation of our collaboration policies, it also ensures you will learn a lot less from this course.
About
EECS 16AB Course Coverage
EECS16AB was specially designed to ramp students up to prepare for courses in machine learning and design and are important classes to set the stage for the rest of your time in the department. A rough breakdown of the content in the classes is as follows:
16A:
Module 1: Introduction to systems and linear algebra
Module 2: Introduction to design and circuit analysis
Module 3: Introduction to machine learning
16B:
Module 1: Differential equations and advanced circuit design
Module 2: Introduction to robotics and control
Module 3: Introduction to unsupervised machine learning and classification
FAQ
Q1: Should I take EECS16A my first semester at Cal?
A1: If you have taken an AP calculus class, then the answer is yes! EECS16A has no prerequisites other than calculus and is designed with freshmen and incoming transfer students in mind. It is designed to be taken alongside 61A. Furthermore, we reserve seats for freshmen and incoming transfer students in the class, so you are essentially guaranteed a spot in the class your first year. It will be harder to get into the class as an upperclassman.
Q2: Should I take EECS 16A and EECS 16B before or after CS 70?
A2: EECS16A and 16B were specifically designed to help ease the transition to CS70 for incoming students. These classes provide an introduction to proofs and the kind of mathematical thinking that is very useful in a class like CS70. We recommend you take 16AB before taking CS70, this should help you have an easier time in CS 70.
Q3: Should I take MATH 54 before taking EECS16A?
A3: EECS 16A is designed to be taken without any prerequisites, so there is no need to take MATH 54 before EECS 16A. EECS 16AB teaches linear algebra with the intent of preparing you for courses like EECS 127 (Optimization) and EECS 189 (Machine Learning) and provides engineering and machine learning examples and applications for linear algebra. EECS 16AB also uses Jupyter notebooks and python so you can better connect linear algebra and computation. There is no need for a CS/EECS student to take Math 54.