Useful links | Schedule
- Lecture: Tu, Thu 11:30 - 12:55 (Searles 126)
- Lab: Fri 11:30 - 12:55 (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.
Textbook (required): Cormen, Leiserson, Rivest and
to Algorithms, 3rd Edition, McGraw Hill, New York, 1990. (bugs).
http://www.bowdoin.edu/~ltoma/teaching/cs231/spring16/. 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 Syllabus.
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 (more) 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, optimization and graph
- We discuss solutions to these problems, while trying to
understand the guiding principles and to illustrate techniques that
can be applied to other problems (e.g. divide-and-conquer,
dynamic programming and greedy).
The class is theoretical and involves no programming.
...and more broadly:
- To be able to analyze the asymptotic performance of algorithms, compare multiple algorithms for a problem and predict
- To be able to argue that an algorithm is correct
- To be familiar with the fundamental algorithms and data
structures and the major design paradigms
- To be able to apply these techniques to new problems
- To get an appreciation of algorithms and an understanding of their importance
- To improve problem solving skills and power of abstraction
- To develop a database of algorithms and techniques which you'll use as building blocks to solve new problems
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 refine them until we
can refine no more. Sometimes it is very effective to see wrong
solutions, 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 instructions compared to
standard lecturing. The process of explaining an argument is
beneficial for everybody involved. You'll work in groups in class,
during labs, and in the study groups.
Expectations This will be a hard class. What
makes it hard is that coming up with algorithms is more an art than a
science: there are no recipes, and each problem is a new
problem. Furthermore, problems that seem very similar, may have very
Plan accordingly, and allow plenty of time to
read the materials and work on the problem sets each week.
To succeed in this class:
- Be pro-active about studying (use class materials and all resources on the internet)
- Formulate questions and attend study groups to 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 prove them correct, or wrong. Take turns.
Office hours, TAs and study groups
Office hours (Toma): Mon, Tue, Thu 10-11
For quick questions you can drop by any time the door is open. If
you cannot come during these times, send me an email to make an
appointment. For specific questions on the assigments, I encourage you
to first talk to the TAs and attend at least one of the study groups,
but preferably more than one.
TAs: Kote Mushegian, Tucker Gordon, Mingo Sanchez, Ethan Zhou
- Sundays: 7-9pm (Tucker)
- Wednesdays: 6-8pm (Kote)
- Thursdays: 5-6pm (Mingo), 7-9pm (Ethan)
Labs and Homework
The lab time is dedicated to more examples, demos and practice
problems. 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.
The assignment is considered an opportunity for learning and
completing the assignment is a learning process. You are not expected
to sit down for a few hours and solve everything in a breath. Instead,
you are expected to view it as a process: read the problems,
understand what they are asking, come up with initial solutions,
figure out why they work or not, 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.
Homeworks, Exams and Grading policy
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 exams, each one about a
third through the semester (precise date TBD); the third exam is
during the final exam period. The exams will be a combination of
in-class and take-home. All exams will be open book and open notes,
and non-cumulative (to the extent possible).
Homework collaboration policy: You are encouraged
to work on problems in a group. You will find that you will gain a
better understanding of the material by discussing the problems with
your partners. However, you must write up the solutions
individually. Limit your collaborators to three or
less, and list the names of the collaborators on the homework.
Grading policy: The final grade is determined as
- Homework assignments (40%).
- 3 exams (60%).
- Class participation.
You are expected to follow Bowdoin's academic honor
code. Collaboration on homeworks is encouraged, however you are
responsible to write the solutions on your own, and list the names of
all your collaborators. You may not glance over someone else's written
solution, and you may not share your problems sets and exams with
anybody else, this term or in the future. Any violation will be
reported and treated according to Bowdoin's academic integrity