Piazza link | Useful links | Detailed schedule | Department's collaboration policy
- Lecture: Tu, Thu 11:30 - 12:55 (Searles 126)
- Lab: Fri 1:00 - 2:25 (Searles 126)
Prerequisites: csci 101 (Intro to CS) and csci
2101 (Data Structures). Generally speaking, a good mathematical
background and good QR skills are not required, but are helpful.
We'll be using the following textbook:
Cormen, Leiserson, Rivest and
to Algorithms, 3rd Edition, McGraw Hill, New York, 1990. (bugs).
http://www.bowdoin.edu/~ltoma/teaching/cs231/2017fall/index.html. Note that
this is a link from my personal website at Bowdoin. This site will
contain all class-related material along the semester. The class does
not have a Blackboard site.
Schedule: For useful links and detailed class
schedule, check detailed schedule.
Office hours, TAs and study groups
We have a fantastic set of TAs who are all eager to work with you:
Office hours and study groups:
- Cory Alini
- Ethan Zhou
- Jack Ward
- Dylan Parsons
- Will DeBruyn
- Duncan Gans
- Office hours Laura: Tue 2:30 to 4, Thu 2:30 to 4. For those who cannot come talk to me on Tue or Thu because of conflicts with other classes, please send me an email to set up meetings on Fri before lab.
- Study groups (in 224)
- Sundays: 7:30-9:30 (Ethan)
- Wednesdays 7:30-9:30 (Cory)
Thursdays 7-9 (Jack)
Problem solving is at the heart of Computer Science and computational
This class is an introduction to problem solving through the design
and analysis of algorithms. We'll introduce some fundamental
algorithmic problems, talk about solutions for these problems, prove
their correctness and analyze their efficiency.
- To start, we'll introduce asymptotic notation, summations and
recurrence relations, as well as basic proof techniques (induction
- We'll discuss fundamental data structures such as search trees,
[augmented search trees], priority queues, [skip lists] and
union-find data structure.
- We'll introduce fundamental algorithmic problems such as
searching, sorting and selection, matrix multiplication, as well as
basic optimization and graph problems.
- We discuss solutions to these problems, while trying to
understand the guiding principles and illustrate techniques that can
be applied to other problems. We'll discuss fundamental algorithmic
techniques: divide-and-conquer, dynamic programming and greedy.
The class is theoretical, however we'll use occasional programming
assignments to help ground theoretical concepts. The work for the
class will be a combination of problems sets and implementation of
algorithms discussed in class.
Homeworks, Exams and grading
Homework: The weekly lab will contain problems to be
solved in the lab, and a set that constitues the homework for the
subsequent week. The homeworks will generally be due a week later
(unless specified otherwise). Late assignments are not accepted
(except in case of medical reasons).
Exams: There will be three in-class exams, all in
All exams are in class and cumulative, but with the emphasis on
the new material that has not been covered in a previous exam. The
exam are closed notes, except one sheet of paper that you can fill in
as you like.
- Exam 1: Thu Oct 12 (11:30am)
- Exam 2: Fri Nov 10 (11:30am)
- Exam 3: Dec 18 (9am)
Grading policy: The final grade is determined as
- 11 lab assignments (20%).
- 3 exams (65%).
- Quizes (announced, in-class) (10%)
- Class participation (5%)
The goal of the class is to give you the necessary tools so that
you are able to design and analyze your own solutions to new
At the end of this class you should:
- Be familiar with the fundamental algorithms and data structures
and the major design paradigms
- Be able to analyze the asymptotic performance of algorithms, compare
multiple algorithms for a problem and predict performance
- Be able to argue correctness of an algorithm
- Be able to come up with algorithms for new problems
- Have developed an appreciation of algorithms and an understanding of
- Have improved problem solving skills and power of abstraction
Teaching style: The goal of the class is not merely
to cover a bunch of algorithms, but rather to emphasize the process of
coming up with solutions. This process is not necessarily neat; it
involves going back and forth, possibly many times, and struggling.
To this end, I will rarely start a class by presenting the
algorithm. Instead, I will start by posing the problem, and asking for
ideas. We'll try to understand properties of the problem, and, as a
group, we'll try to come up with solutions and gradually refine them.
Sometimes it is very effective to see ideas that don't work, or ideas
that lead nowhere.
As we come up with solutions, we'll try to generalize and derive
"techniques" that we can apply to other problems.
The most important skill to learn is abstraction and problem solving:
the ability to think critically and solve new problems.
The course relies heavily on group work and peer instruction, so it
is crucial that you attend all classes and all labs. There is a lot
of research that shows the benefits of peer instruction compared to
standard lecturing. The process of explaining an argument is
beneficial for everybody involved. You'll work in groups in class and
Expectations You will probably find this class to be
difficult. What makes it hard is that this material is new and
different than Data Structures, and spans many levels of
abstractions. Coming up with algorithms is more an art than a science:
there is no systematic way to have an idea, or even to tell what sort
of technique would work for a given problem. Problems that seem very
similar, may have very different solutions.
It is important that you know this ahead of time and you start
preparing mentally for a difficult course.
That said, I believe in a growth mindset: if the material comes
easy to you, it's because you have the right type of background and
experience. If you have difficulty in the class, it's not because you
are not smart, but because you don't have the right type of
background----you'll just need to work harder to build it. You can do
Here are some suggestions for doing well in class:
- Plan accordingly, and allow plenty of time to
read the materials and work on the problem sets each week.
- Be pro-active about studying (use class materials and search for
resources on the internet)
- Encourage self-reflection: Try to formulate questions, and try to answer them yourself.
- Go to the study groups and talk to the TAs; Listen to your peers'
questions and get your questions answered.
- Solve all problems that are assigned in class and lab, even those
that are optional.
- Find a group of peers to work with. Explain your ideas to your
peers, and listen to their ideas. Try to argue why a solution is
correct, or prove it wrong by finding an instance where it does not
work (a counter-example). Take turns.
- Practice. Practice. Practice.
- Don't be harsh on yourself if you are not doing as well as you
expected. Push yourself, don't focus on grades, enjoy the process!
The lab time is dedicated to working through (more) examples, demos
and practice problems, and answering any questions that may come up during
lectures. A lab will usually contain a set of problems to be
completed in the lab, and a problem set that becomes the homework
assignment for the following week.
Occasionaly we'll swap a lecture with a lab, that is, we'll do
lecturing during labs, and lab-work during lectures.
The assignments will contain not only applications of the problems
discussed in class, but also new problems that will require creative
new ideas. Completing the assignment is a learning process (as
you probably know from Data Structures). Do not expect to sit down
for a few hours and solve everything at once (if you do, come talk to
me!). Instead, expect a process: read the problems, understand what
they are asking, come up with initial solutions, figure out why they
work or not, try to formulate questions, come up with
improvements. The whole process is supposed to be interactive between
you, the group of people you collaborate withm your TAs, and myself.
Homework collaboration policy and academic integrity
All students are expected to be familiar withm and to comply with the department's collaboration policy.
Collaboration and discussion are crucial for this class. You are
encouraged to work on problems in a group and you will most likely find that you
will gain a better understanding of the material by discussing the
problems with your partners.
If you collaborate, you need to list the names of the collaborators on
the front page of the homework.
However, our goal is to ensure that the collaboration is
appropriate and effective, and that you become an independent problem
solver capable to do work on your own.
official collaboration policy defines four levels of
Specifically for this class,
- All in-class and in-lab work, unless otherwise specified, is at
collaboration-level 0; that is, no restrictions.
- The homework assignments, unless otherwise specified, will be at
collaboration-level 1; that is, verbal collaboration without
solution sharing. You are allowed and encouraged to discuss ideas with
other class members, but the communication should be verbal and
additionally it can include diagrams on board. Noone is allowed to
take notes during the discussion (being able to recreate the solution
later frommemory is proof that you actually understood
it). Communication cannot include sharing pseudocode for the problem.
- Exams and quizes will be at collaboraytion-level 3
(professor clarifications only).
While peer instruction can be immensely useful, it an also
harm. Once you found a solution, resist giving hints to your peers or
leading them towards the answer. You are not helping them by doing
so. Direct them towards the TAs who are trained to give help.
Remember that you are responsible for reading, understanding, and
adhering to the department policy. If you have any questions about any aspects of
the policy, please do not hesitate to ask for clarification.