EECS16A, Designing Information Devices and Systems I
5/11/2021: The Final will take place on May 12 from 11:30 AM - 2:30 PM PT You can find the answer template here.
Please note that Youtube videos will require that you are signed into a berkeley.edu account. Otherwise, you'll see some indication of the video being private. The schedule below is subject to change; for deviations from the schedule, see above.
Note: Due to Zoom's password length constraints, the password for cloud recordings for discussion will be eecs16a! .
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NotesGrey notes are *still relevant material* for the course! They simply have not yet been covered in lecture. Blue notes have been covered in lecture. Notes with an [updated] tag to their left have been changed since last semester's iteration. Be aware that the un-updated notes are subject to change.
- Note 0 - Introduction
- 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 Touchscreens and Comparators
- Note 17B - Charge Sharing
- Note 18 - Op-Amps in Negative Feedback
- Note 19 - More Op-Amp Topologies
- Note 20 - Op-Amp 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
- Note 25 - More Trilateration
Office hours and HW Party are held here.
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NOTE (1/18/21): Calendar events for this semester are still being added in. Please be patient if you see missing events. Thanks!
Please note the important information below the table. The set of ASEs assisting with a given discussion (if any) is given by the bulleted list.
|Time (Mon/Wed)||Lecture Style||Individual Worktime||Group Section|
[PRIOR LINEAR ALGEBRA EXPERIENCE]
Note: Monday and Wednesday discussion sections cover different material, and you are very strongly encouraged to go to a discussion both days. Scroll horizontally to view entire table, and see below for critical information on how to read the table.
To account for different learning styles, there will be 3 different styles of discussion sections.
- The first set of sections are “Group Sections" (blue) . These discussion sections will be staffed with a TA and many ASEs, and students will be given a chance to work with each other in breakout rooms.
- The second type of sections are "Individual Worktime" (green) , which are more oriented toward individual work and are intended for students who prefer working solo and not in groups. Here, the TA will give you time to work on the problem on your own and then discuss the answer.
- The last type of section (similar to the second type) is "Lecture Style" (purple) . There may be slightly less time for individual work on the problems in the Lecture Style sessions, but TAs will be there to answer questions in all sessions.
Furthermore note the following; despite the bold labels in some sections, all sections are open to all. However, to facilitate similar groups of students getting to know each other, we have designated sections according to categories. Freshman section or Freshman/Sophomore sections are intended for these specific years of students. Transfer sections are intended for Transfer students. The Linear Algebra experience section is intended for upper division students who might have some prior linear algebra experience. Again, all sections are open to all.
Piazza (Ask Questions Here)
This book consists of condensed sets of notes that summarize the important material from the course notes, as well as detailed solutions for the online Practice Problems! Here's the entire book and the Table of Contents. Individual chapters of the book (notes and solutions by practice set) can be found here (the links aren't perfect, you may need to scroll a tiny bit down for some chapters). A couple brief comments on using this resource:
- I recommend skimming the Introductory Chapter and the Conventions Chapter. These will provide some useful tips to keep in mind.
- Each chapter has a Relevant Information section and a Problems section. The first contains a (generally complete) summary of the corresponding content from the notes. The second contains the detailed solutions mentioned above.
- This is a new resource, and may well have errors or areas to improve in; if you spot something wrong and would like to mention it, or have feedback of any kind, please submit a feedback ticket.
Technology Needs (STEP)
Student Technology Equity Program (STEP). STEP provides laptops and other technologies for free and is for undergraduate, graduate, and professional students. It requires just a simple online application form. For details, see here.
- EE16A's Guide to the Recommended Texts
- ELECTRONICS Reader (50MB) by Ali M. Niknejad, or the 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. (instructions to login to the university network from home here )
- Recipe: Nodal Analysis!
- Recipe: Charge Sharing!
- Charge-Sharing Algorithm (Sp20)
- Recipe: Thevenin and Norton Equivalents! (INCOMPLETE)
- Recipe: Design Topologies!
- Step-By-Step Gaussian Elimination by Andi Gu, a former student. Has at least one very minor bug regarding labeling of row operations.
- studEE16A (may need to load each page twice to view the LaTeX)
- Fun with Stacked Caps
- EECS16A Lab Equipment Guide
- Review of Past Proofs
- Fall 2020 Discussion Checkoffs: Questions and Answers
Setting up How-To's
Past ExamsPast exams vary in scope from semester to semester, and may include topics that are not in scope for the current semester or module. Unavailable exams are indicated by N/A. In-scope topics for the current semester will be posted on Piazza about a week before the corresponding exam.
|Semester||Midterm 1||Midterm 2||Final|
|fa20||pdf, sol||pdf, sol||pdf, sol|
|su20||pdf, sol||pdf, sol||pdf, sol|
|sp20||pdf, sol||pdf, sol||pdf, sol|
|fa19||pdf, sol||pdf, sol||pdf, sol|
|sp19||pdf, sol||pdf, sol||pdf, sol|
|fa18||pdf, sol||pdf, sol||pdf, sol|
|sp18||pdf, sol||pdf, sol||pdf, sol|
|fa17||pdf, sol||pdf, sol||pdf, sol|
|su17||pdf, sol||pdf, sol||N/A|
|sp17||pdf, sol||pdf, sol||N/A|
|fa16||pdf, sol||pdf, sol||pdf, sol|
|sp16||pdf, sol||pdf, sol||pdf, sol|
|fa15||pdf, sol||pdf, sol||pdf, sol|
|sp15||pdf, sol||pdf, sol||pdf, sol|
Simulations and DemosThis is a running list of simulations and demos that have been created in recent semesters (in rough order of appearance).
Practice Sets: Links to Notes and Solutions
It is very strongly recommend that you try the problems themselves here before looking at the solutions below. The links for solutions are not perfect, so you may need to scroll to the bottom of the linked page to find them. Give feedback here.
CSM MaterialsBelow are the CSM worksheets that have been released for the current semester.
|1||Gaussian Elimination, Matrix-Vector Operations, Linearity, and Span||sol|
|2||Proofs, Transition Matrices, and Invertibility||sol|
|4||Eigenvectors, Eigenvalues, and PageRank||sol|
|5||Passive Sign Convention, and NVA||sol|
|6||Resistivity and Equivalence||sol|
|7||Superposition and Capacitance||sol|
|8||Touchscreens and Charge Sharing||sol|
|9||Comparators, Op Amps and Circuit Design||sol|
|10||Vector Norms, Products, and Correlation||sol|
|11||Vector Norms, Products, and Correlation||sol|
Course StaffPlease add berkeley.edu to the end of all emails!
PoliciesFor a full list of course policies and the syllabus, see here.
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:
Module 1: Introduction to systems and linear algebra
Module 2: Introduction to design and circuit analysis
Module 3: Introduction to machine learning
Module 1: Differential equations and advanced circuit design
Module 2: Introduction to robotics and control
Module 3: Introduction to unsupervised machine learning and classification
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.