Online course materials
Course
Statistics
Materials
Course
Materials (Free)
(Instructor
videos, Learn by
doing exercises)
University
San José
State
University /
Udacity
Link
https://ww
w.udacity.c
om/course/
st095
Introductio
n to
Probability
and
Statistics
Course
materials
(2005)
Massachuse
tts Institute
of
Technology
Probability
& Statistics
Enter Course
(Open + Free)
Carnegie
Mellon
University
http://ocw.
mit.edu/co
urses/math
ematics/1805introductio
n-toprobabilityandstatisticsspring2005/
http://oli.c
mu.edu/cou
rses/freeopen/statist
Description
We live in a time of unprecedented access to
information...data. Whether researching the best
school, job, or relationship, the Internet has thrown
open the doors to vast pools of data. Statistics are
simply objective and systematic methods for describing
and interpreting information so that you may make the
most informed decisions about life.
This course provides an elementary introduction to
probability and statistics with applications. Topics
include: basic probability models; combinatorics;
random variables; discrete and continuous probability
distributions; statistical estimation and testing;
confidence intervals; and an introduction to linear
regression.
This course introduces students to the basic concepts
and logic of statistical reasoning and gives the students
introductory-level practical ability to choose, generate,
and properly interpret appropriate descriptive and
Compensation
Examiner
Statistical
Reasoning
Enter Course
(Open + Free)
Carnegie
Mellon
University
ics-coursedetails/
inferential methods. In addition, the course helps
students gain an appreciation for the diverse
applications of statistics and its relevance to their lives
and fields of study. The course does not assume any
prior knowledge in statistics and its only prerequisite is
basic algebra. We offer two versions of statistics, each
with a different emphasis: Probability and
Statistics andStatistical Reasoning. Each course includes
all expository text, simulations, case studies,
comprehension tests, interactive learning exercises,
and the StatTutor labs. Each course contains all of the
instructions for the four statistics packages options we
support. To do the activities, you will need your own
copy of Microsoft Excel, Minitab, the open source R
software, TI calculator, or StatCrunch. One of the main
differences between the courses is the path through
probability. Probability and Statistics includes the
classical treatment of probability as it is in the earlier
versions of the OLI Statistics course.
http://oli.c
mu.edu/cou
rses/freeopen/statist
icalreasoningcoursedetails/
Statistical Reasoning introduces students to the basic
concepts and logic of statistical reasoning and gives the
students introductory-level practical ability to choose,
generate, and properly interpret appropriate
descriptive and inferential methods. In addition, the
course helps students gain an appreciation for the
diverse applications of statistics and its relevance to
their lives and fields of study. The course does not
assume any prior knowledge in statistics and its only
prerequisite is basic algebra.
We offer two versions of statistics, each with a
different emphasis: Probability and
Statistics andStatistical Reasoning. Each course includes
all expository text, simulations, case studies,
comprehension tests, interactive learning exercises,
and the StatTutor labs. Each course contains all of the
instructions for the four statistics packages options we
support. To do the activities, you will need your own
copy of Microsoft Excel, Minitab, the open source R
software, TI calculator, or StatCrunch.
One of the main differences between the courses is the
path through probability; Statistical Reasoning places
less emphasis on probability than does the Probability
and Statistics course and takes an empirical approach.
Introductio
n to
Computer
Science
Introductio
n to
Computer
Science I
Course
Materials (Free)
Introductio
Course
Anytime, selfpaced
Udacity
https://ww
w.udacity.c
om/course/
cs101
Harward
https://ww
University
w.edx.org/c
ourses/Harv
ardX/CS50x
/2012/abou
t
Massachuse http://ocw.
In this course you will learn key concepts in computer
science and learn how to write your own computer
programs in the context of building a web crawler.
access denied
This subject is aimed at students with little or no
n to
Computer
Science and
Programmi
ng
Materials (2008) tts Institute
of
Technology
Computer
Science
Course
Materials
KHANAcade
my
Principles
of
Computing
Enter Course
(Open + Free)
Carnegie
Mellon
University
mit.edu/co
urses/electr
icalengineering
-andcomputerscience/600introductio
n-tocomputerscienceandprogrammi
ng-fall2008/
https://ww
w.khanacad
emy.org/cs
programming experience. It aims to provide students
with an understanding of the role computation can play
in solving problems. It also aims to help students,
regardless of their major, to feel justifiably confident of
their ability to write small programs that allow them to
accomplish useful goals. The class will use the Python™
programming language.
Learn the fundamentals of programming on the Khan
Academy Computer Science platform. Explore
programs made by others. Write your own programs
and share them!
http://oli.c This course covers elementary principles of computing,
mu.edu/cou including iteration, recursion, and binary
rses/freerepresentation of data. Additional topics on cellular
open/comp automata, encryption, and the limits of computation
utingare also introduced. The goal of this course is to
courseintroduce some of the techniques used in computer
details/
science to solve complex problems, with or without a
computer. This course does not include a programming
Media
Programmi
ng
Enter Course
(Open + Free)
Carnegie
Mellon
University
Ohjelmoinn
in MOOC
Creative
Commons BYNC-SA-lisenssillä
lisensoitu
materiaali
Helsingin
yliopiston
TKTL
component, although the principles that are taught can
be used in a programming context.
http://oli.c Programming is a way of organizing a task so that it is
mu.edu/cou replicable by something else—a computer. If you have
rses/freeever given someone directions, or written down a
open/media recipe, you have some experience with programming.
Learning more about programming will help you
programmi develop the skills of thinking systematically about a
ng-coursetask and breaking it down into manageable pieces,
details/
which can be applied in many disciplines.
This class contextualizes the task of programming by
focusing on media, such as images, audio, and
interactive systems. By doing so, we hope to put
programming in a relevant context. For example,
iteration is a programming concept that is essential to
creating negative and grayscale images. You will learn
algorithms for blending two images together and how
to hierarchical relationships are used to organize
elements of a user interface.
This introductory course has no particular prerequisites
and is primarily designed for non-computer science
students.
http://moo
c.cs.helsinki
.fi/ohjelmoi
nti
ObjectOriented
programmi
ng with
Java, part I
Peliohjelmo
innin
MOOC
Course
Materials
Helsingin
yliopiston
TKTL
http://moo
c.cs.helsinki
.fi/program
ming-part1
Course
Materials
Helsingin
yliopiston
TKTL
Algoritmien Course
MOOC
Materials
Helsingin
yliopiston
TKTL
Algorithms
Rutgers
University
http://moo
c.cs.helsinki
.fi/peliohjel
mointi
http://moo
c.cs.helsinki
.fi/algoritmi
t
https://ww
w.udacity.c
om/course/
cs215
Introductio
n to
Algorithms
Course
Materials (Free)
– Enroll in
Course
($199/month aft
er 14-day trial)
Course
Materials (2011)
Massachuse
tts Institute
of
Technology
http://ocw.
mit.edu/co
urses/electr
icalengineering
-andcomputerscience/6006-
Ever played the Kevin Bacon game? This class will show
you how it works by giving you an introduction to the
design and analysis of algorithms, enabling you to
discover how individuals are connected.
This course provides an introduction to mathematical
modeling of computational problems. It covers the
common algorithms, algorithmic paradigms, and data
structures used to solve these problems. The course
emphasizes the relationship between algorithms and
programming, and introduces basic performance
measures and analysis techniques for these problems.
introductio
n-toalgorithmsfall-2011/
Computer
Course
Massachuse http://ocw.
Algorithms Materials (2010) tts Institute mit.edu/co
in Systems
of
urses/civilEngineering
Technology andenvironmen
talengineering
/1-204computeralgorithmsin-systemsengineering
-spring2010/index.
htm
Advanced
Dowload Course Massachuse http://ocw.
Algorithms Materials (2008) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6854j-
This course covers concepts of computation used in
analysis of engineering systems. It includes the
following topics: data structures, relational database
representations of engineering data, algorithms for the
solution and optimization of engineering system
designs (greedy, dynamic programming, branch and
bound, graph algorithms, nonlinear optimization), and
introduction to complexity analysis. Object-oriented,
efficient implementations of algorithms are
emphasized.
This is a graduate course on the design and analysis of
algorithms, covering several advanced topics not
studied in typical introductory courses on algorithms. It
is especially designed for doctoral students interested
in theoretical computer science.
Introductio
n to
Theoretical
Computer
Science
Interactive
3D Graphics
Course
materials
Udacity
Course
Udacity
Materials (Free)
– Enroll in
Course
($199/month aft
er 14-day trial)
advancedalgorithmsfall-2008/
https://ww
w.udacity.c
om/course/
cs313
This class teaches you about basic concepts in
theoretical computer science -- such as NPcompleteness -- and what they imply for solving tough
algorithmic problems.
https://ww
w.udacity.c
om/course/
cs291
This class will teach you about the basic principles of 3D
computer graphics: meshes, transforms, cameras,
materials, lighting, and animation.
Course content is brought to you in partnership with
Autodesk, a worldwide leader in 3D design,
engineering, and entertainment
software. http://www.autodesk.com
Computatio Course
Massachuse http://ocw.
nal
Materials (2003) tts Institute mit.edu/co
Geometry
of
urses/mech
Technology anicalengineering
/2-158jcomputatio
nalgeometryspring2003/
Topics in surface modeling: b-splines, non-uniform
rational b-splines, physically based deformable
surfaces, sweeps and generalized cylinders, offsets,
blending and filleting surfaces. Non-linear solvers and
intersection problems. Solid modeling: constructive
solid geometry, boundary representation, non-manifold
and mixed-dimension boundary representation models,
octrees. Robustness of geometric computations.
Interval methods. Finite and boundary element
discretization methods for continuum mechanics
problems. Scientific visualization. Variational geometry.
Tolerances. Inspection methods. Feature
Computer
Course
Massachuse http://ocw.
System
Materials (2009) tts Institute mit.edu/co
Engineering
of
urses/electr
Technology icalengineering
-andcomputerscience/6033computersystemengineering
-spring2009/
Programmi Course
Udacity
https://ww
ng
Materials (Free)
w.udacity.c
Languages
– Enroll in
om/course/
Course
cs262
($199/month aft
er 14-day trial)
representation and recognition. Shape interrogation for
design, analysis, and manufacturing. Involves analytical
and programming assignments.
This course was originally offered in Course 13
(Department of Ocean Engineering) as 13.472J. In 2005,
ocean engineering subjects became part of Course 2
(Department of Mechanical Engineering), and this
course was renumbered 2.158J.
This course covers topics on the engineering of
computer software and hardware systems: techniques
for controlling complexity; strong modularity using
client-server design, virtual memory, and threads;
networks; atomicity and coordination of parallel
activities; recovery and reliability; privacy, security, and
encryption; and impact of computer systems on
society. Case studies of working systems and readings
from the current literature provide comparisons and
contrasts. Two design projects are required, and
students engage in extensive written communication
exercises.
This class will give you an introduction to the
fundamentals of programming languages. Key concepts
include how to specify and process valid strings,
sentences and program structures.
Design of
Computer
Programs
Computer
System
Architectur
e
Artificial
Intelligence
for
Robotics
Acces Course
Udacity
Materials (Free)
– Enroll in
Course
($199/month aft
er 14-day trial)
Course
Massachuse
Materials (2005) tts Institute
of
Technology
Course
Astetta
Materials (Free) vaativampi
– Enroll in
Course
($199/month aft
er 14-day trial)
https://ww
w.udacity.c
om/course/
cs212
Learn new concepts, patterns, and methods that will
expand your programming abilities, helping move you
from a novice to an expert programmer.
http://ocw.
mit.edu/co
urses/electr
icalengineering
-andcomputerscience/6823computersystemarchitecture
-fall-2005/
https://ww
w.udacity.c
om/course/
cs373
6.823 is a course in the department's "Computer
Systems and Architecture" concentration. 6.823 is a
study of the evolution of computer architecture and
the factors influencing the design of hardware and
software elements of computer systems. Topics may
include: instruction set design; processor microarchitecture and pipelining; cache and virtual memory
organizations; protection and sharing; I/O and
interrupts; in-order and out-of-order superscalar
architectures; VLIW machines; vector supercomputers;
multithreaded architectures; symmetric
multiprocessors; and parallel computers.
Learn how to program all the major systems of a
robotic car from the leader of Google and Stanford's
autonomous driving teams. This class will teach you
basic methods in Artificial Intelligence, including:
probabilistic inference, planning and search,
localization, tracking and control, all with a focus on
robotics. Extensive programming examples and
assignments will apply these methods in the context of
building self-driving cars.
Jatkokurssi
kurssille
TIEP114
Tietokoneen
rakenne ja
arkkitehtuuri (3
op)
(tai
korvaa
kurssin TIEP114
ellei sitä ole ei
suoritettu)
Ari
Viinikainen
Cryptograp
hy and
Cryptanalys
is
Network
and
Computer
Security
Course
Massachuse http://ocw.
Materials (2005) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6875cryptograph
y-andcryptanalysi
s-spring2005/index.
htm
Course
Massachuse http://ocw.
Materials (2003) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6857networkandcomputersecurity-
This course features a rigorous introduction to modern
cryptography, with an emphasis on the fundamental
cryptographic primitives of public-key encryption,
digital signatures, pseudo-random number generation,
and basic protocols and their computational complexity
requirements.
6.857 is an upper-level undergraduate, first-year
graduate course on network and computer security. It
fits within the department's Computer Systems and
Architecture Engineering concentration. Topics covered
include (but are not limited to) the following:
- Techniques for achieving security in multi-user
computer systems and distributed computer systems;
- Cryptography: secret-key, public-key, digital
signatures;
- Authentication and identification schemes;
- Intrusion detection: viruses;
- Formal models of computer security;
- Secure operating systems;
TIES327
Tietoverkkoturv
allisuus (3-5 op)
Ari
Viinikainen
fall-2003/
Applied
Cryptograp
hy
Selected
Topics in
Cryptograp
hy
Software
Debugging
Access Course
Materials
Udacity
https://ww
w.udacity.c
om/course/
cs387
Course
Massachuse http://ocw.
Materials (2004) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6897selectedtopics-incryptograph
y-spring2004/
Course
Udacity
https://ww
Materials (Free)
w.udacity.c
– Enroll in
om/course/
- Software protection;
- Security of electronic mail and the World Wide Web;
- Electronic commerce: payment protocols, electronic
cash;
- Firewalls; and
- Risk assessment.
Cryptography is present in everyday life, from paying
with a credit card to using the telephone. Learn all
about making and breaking puzzles in computing.
This course covers a number of advanced "selected
topics" in the field of cryptography. The first part of the
course tackles the foundational question of how to
define security of cryptographic protocols in a way that
is appropriate for modern computer networks, and
how to construct protocols that satisfy these security
definitions. For this purpose, the framework of
"universally composable security" is studied and used.
The second part of the course concentrates on the
many challenges involved in building secure electronic
voting systems, from both theoretical and practical
points of view. In the third part, an introduction to
cryptographic constructions based on bilinear pairings
is given.
In this class you will learn how to debug programs
systematically, how to automate the debugging process
and build several automated debugging tools in Python.
Software
Testing
Data
Mining
Course
($199/month aft
er 14-day trial)
Course
Udacity
Materials
cs259
https://ww
w.udacity.c
om/course/
cs258
Course
Massachuse http://ocw.
Materials (2003) tts Institute mit.edu/co
of
urses/sloanTechnology school-ofmanageme
nt/15-062dataminingspring2003/
When writing software, destruction can be just as
valuable as creation. Learn how to catch bugs and
break software as you discover different testing
methods that will help you build better software.
Data that has relevance for managerial decisions is
accumulating at an incredible rate due to a host of
technological advances. Electronic data capture has
become inexpensive and ubiquitous as a by-product of
innovations such as the internet, e-commerce,
electronic banking, point-of-sale devices, bar-code
readers, and intelligent machines. Such data is often
stored in data warehouses and data marts specifically
intended for management decision support. Data
mining is a rapidly growing field that is concerned with
developing techniques to assist managers to make
intelligent use of these repositories. A number of
successful applications have been reported in areas
such as credit rating, fraud detection, database
marketing, customer relationship management, and
stock market investments. The field of data mining has
evolved from the disciplines of statistics and artificial
intelligence.
This course will examine methods that have emerged
from both fields and proven to be of value in
recognizing patterns and making predictions from an
Statistics
and
Visualizatio
n for Data
Analysis
and
Inference
Resourse
Massachuse http://ocw.
Materials (2009) tts Institute mit.edu/res
of
ources/resTechnology 9-0002statisticsandvisualizatio
n-for-dataanalysisandinferencejanuary-iap2009/
Introductio Course
Udacity
https://ww
n to
Materials (Free)
w.udacity.c
Artificial
– Enroll in
om/course/
Intelligence Course
cs271
($199/month aft
er 14-day trial)
Introductio Course
Massachuse http://ocw.
n to
Materials (2010) tts Institute mit.edu/co
Communica
of
urses/electr
tion,
Technology icalControl,
engineering
applications perspective. We will survey applications
and provide an opportunity for hands-on
experimentation with algorithms for data mining using
easy-to- use software and cases.
A whirl-wind tour of the statistics used in behavioral
science research, covering topics including: data
visualization, building your own null-hypothesis
distribution through permutation, useful parametric
distributions, the generalized linear model, and modelbased analyses more generally. Familiarity with
MATLAB®, Octave, or R will be useful, prior experience
with statistics will be helpful but is not essential. This
course is intended to be a ground-up sketch of a
coherent, alternative perspective to the "nullhypothesis significance testing" method for behavioral
research (but don't worry if you don't know what this
means).
The objective of this class is to teach you modern AI.
You will learn about the basic techniques and tricks of
the trade. We also aspire to excite you about the field
of AI.
This course examines signals, systems and inference as
unifying themes in communication, control and signal
processing. Topics include input-output and state-space
models of linear systems driven by deterministic and
random signals; time- and transform-domain
and Signal
Processing
Signal
Processing:
Continuous
and
Discrete
DiscreteTime Signal
Processing
-andcomputerscience/6011introductio
n-tocommunica
tioncontroland-signalprocessingspring2010/
Course
Massachuse http://ocw.
Materials (2008) tts Institute mit.edu/co
of
urses/mech
Technology anicalengineering
/2-161signalprocessingcontinuousanddiscretefall-2008/
Course
Massachuse http://ocw.
Materials (2005) tts Institute mit.edu/co
of
urses/electr
representations in discrete and continuous time; group
delay; state feedback and observers; probabilistic
models; stochastic processes, correlation functions,
power spectra, spectral factorization; least-mean
square error estimation; Wiener filtering; hypothesis
testing; detection; matched filters.
This course provides a solid theoretical foundation for
the analysis and processing of experimental data, and
real-time experimental control methods. Topics
covered include spectral analysis, filter design, system
identification, and simulation in continuous and
discrete-time domains. The emphasis is on practical
problems with laboratory exercises.
TIES324 Signaali
nkäsittely, 4 op
Ari
Viinikainen
This class addresses the representation, analysis, and
design of discrete time signals and systems. The major
concepts covered include: Discrete-time processing of
TIES324 Signaali
nkäsittely, 4 op
Ari
Viinikainen
Technology
Digital
Signal
Processing
icalengineering
-andcomputerscience/6341discretetime-signalprocessingfall-2005/
Course
Massachuse http://ocw.
Materials (2011) tts Institute mit.edu/res
of
ources/resTechnology 6-008digitalsignalprocessingspring2011/
continuous-time signals; decimation, interpolation, and
sampling rate conversion; flowgraph structures for DT
systems; time-and frequency-domain design
techniques for recursive (IIR) and non-recursive (FIR)
filters; linear prediction; discrete Fourier transform, FFT
algorithm; short-time Fourier analysis and filter banks;
multirate techniques; Hilbert transforms; Cepstral
analysis and various applications.
This course was developed in 1987 by the MIT Center
TIES324 Signaali
for Advanced Engineering Studies. It was designed as a nkäsittely, 4 op
distance-education course for engineers and scientists
in the workplace.
Advances in integrated circuit technology have had a
major impact on the technical areas to which digital
signal processing techniques and hardware are being
applied. A thorough understanding of digital signal
processing fundamentals and techniques is essential for
anyone whose work is concerned with signal processing
applications.
Digital Signal Processing begins with a discussion of the
analysis and representation of discrete-time signal
systems, including discrete-time convolution,
difference equations, the z-transform, and the discretetime Fourier transform. Emphasis is placed on the
similarities and distinctions between discrete-time. The
course proceeds to cover digital network and
Ari
Viinikainen
Signals and
Systems
Machine
Vision
Course
Massachuse http://ocw.
Materials (2011) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6003-signalsandsystemsfall-2011/
Course
Massachuse http://ocw.
Materials (2004) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6801machinevision-fall-
nonrecursive (finite impulse response) digital filters.
Digital Signal Processing concludes with digital filter
design and a discussion of the fast Fourier transform
algorithm for computation of the discrete Fourier
transform.
6.003 covers the fundamentals of signal and system
analysis, focusing on representations of discrete-time
and continuous-time signals (singularity functions,
complex exponentials and geometrics, Fourier
representations, Laplace and Z transforms, sampling)
and representations of linear, time-invariant systems
(difference and differential equations, block diagrams,
system functions, poles and zeros, convolution, impulse
and step responses, frequency responses). Applications
are drawn broadly from engineering and physics,
including feedback and control, communications, and
signal processing.
Machine Vision provides an intensive introduction to
the process of generating a symbolic description of an
environment from an image. Lectures describe the
physics of image formation, motion vision, and
recovering shapes from shading. Binary image
processing and filtering are presented as preprocessing
steps. Further topics include photogrammetry, object
representation alignment, analog VLSI and
computational vision. Applications to robotics and
intelligent machine interaction are discussed.
2004/
Pattern
Course
Massachuse http://ocw.
Recognition Materials (2004) tts Institute mit.edu/co
for
of
urses/brainMachine
Technology andVision
cognitivesciences/9913patternrecognitionformachinevision-fall2004/
Systems
Course
Massachuse http://ocw.
Optimizatio Materials (2003) tts Institute mit.edu/co
n
of
urses/sloanTechnology school-ofmanageme
nt/15-057systemsoptimizatio
n-spring2003/index.
htm
The applications of pattern recognition techniques to
problems of machine vision is the main focus for this
course. Topics covered include, an overview of
problems of machine vision and pattern classification,
image formation and processing, feature extraction
from images, biological object recognition, bayesian
decision theory, and clustering.
Managers and engineers are constantly attempting to
optimize, particularly in the design and operation of
complex systems. This course is an application-oriented
introduction to (systems) optimization. It seeks to:
- Motivate the use of optimization models to support
managers and engineers in a wide variety of decision
making situations;
- Show how several application domains (industries)
use optimization;
- Introduce optimization modeling and solution
techniques (including linear, non-linear, integer, and
network optimization, and heuristic methods);
- Provide tools for interpreting and analyzing modelbased solutions (sensitivity and post-optimality
Optimizatio
n Methods
Nonlinear
Programmi
ng
Introductio
n to
analysis, bounding techniques); and
- Develop the skills required to identify the opportunity
and manage the implementation of an optimizationbased decision support tool.
Course
Massachuse http://ocw. This course introduces the principal algorithms for
Materials (2009) tts Institute mit.edu/co linear, network, discrete, nonlinear, dynamic
of
urses/sloan- optimization and optimal control. Emphasis is on
Technology school-ofmethodology and the underlying mathematical
manageme structures. Topics include the simplex method, network
nt/15-093j- flow methods, branch and bound and cutting plane
optimizatio methods for discrete optimization, optimality
n-methods- conditions for nonlinear optimization, interior point
fallmethods for convex optimization, Newton's method,
2009/index. heuristic methods, and dynamic programming and
htm
optimal control methods.
Course
Massachuse http://ocw. This course introduces students to the fundamentals of
Materials (2004) tts Institute mit.edu/co nonlinear optimization theory and methods. Topics
of
urses/sloan- include unconstrained and constrained optimization,
Technology school-oflinear and quadratic programming, Lagrange and conic
manageme duality theory, interior-point algorithms and theory,
nt/15-084j- Lagrangian relaxation, generalized programming, and
nonlinearsemi-definite programming. Algorithmic methods used
programmi in the class include steepest descent, Newton's
ng-springmethod, conditional gradient and subgradient
2004/index. optimization, interior-point methods and penalty and
htm
barrier methods.
Course
Massachuse http://ocw. 6.336J is an introduction to computational techniques
Materials (2003) tts Institute mit.edu/co for the simulation of a large variety of engineering and
Numerical
Simulation
of
Technology
urses/electr
icalengineering
-andcomputerscience/6336jintroductio
n-tonumericalsimulationsma-5211fall-2003/
Functional
Hardware
Verification
Course
Materials
Udacity
https://ww
w.udacity.c
om/course/
cs348
Introductio
n to Parallel
Programmi
ng
Course
Udacity
Materials (Free)
– Enroll in
Course
($199/month aft
https://ww
w.udacity.c
om/course/
cs344
physical systems. Applications are drawn from
aerospace, mechanical, electrical, chemical and
biological engineering, and materials science. Topics
include: mathematical formulations; network
problems; sparse direct and iterative matrix solution
techniques; Newton methods for nonlinear problems;
discretization methods for ordinary, time-periodic and
partial differential equations, fast methods for partial
differential and integral equations, techniques for
dynamical system model reduction and approaches for
molecular dynamics.
This course was also taught as part of the SingaporeMIT Alliance (SMA) programme as course number SMA
5211 (Introduction to Numerical Simulation).
When developing chips it is essential that they get
verified thoroughly because it is very hard or
impossible to fix them once they have been
manufactured. In this class, you will learn how to
program verification environments that verify chip
functionality efficiently, as well as understand and
leverage automation such as constrained random test
generation and improve code reuse leveraging a
standardized methodology.
Learn the fundamentals of parallel computing with the
GPU and the CUDA programming environment! In this
class, you'll learn about parallel programming by coding
a series of image processing algorithms, such as you
might find in Photoshop or Instagram. You'll be able to
er 14-day trial)
Parallel
Computing
Theory of
Parallel
Systems
Course
Massachuse http://ocw.
Materials (2011) tts Institute mit.edu/co
of
urses/math
Technology ematics/18337jparallelcomputingfall-2011/
Course
Massachuse http://ocw.
Materials (2011) tts Institute mit.edu/co
of
urses/electr
Technology icalengineering
-andcomputerscience/6895-theoryof-parallelsystemssma-5509-
program and run your assignments on high-end GPUs,
even if you don't own one yourself.
Why It’s Important to Think Parallel
Third Pillar of Science
Learn how scientific discovery can be accelerated by
combining theory and experimentation with computing
to fight cancer, prevent heart attacks, and spur new
advances in robotic surgery.
This is an advanced interdisciplinary introduction to
applied parallel computing on modern supercomputers.
It has a hands-on emphasis on understanding the
realities and myths of what is possible on the world's
fastest machines. We will make prominent use of
the Julia Language software project.
6.895 covers theoretical foundations of generalpurpose parallel computing systems, from languages to
architecture. The focus is on the algorithmic
underpinnings of parallel systems. The topics for the
class will vary depending on student interest, but will
likely include multithreading, synchronization, race
detection, load balancing, memory consistency, routing
networks, message-routing algorithms, and VLSI layout
theory. The class will emphasize randomized algorithms
and probabilistic analysis, including high-probability
arguments.
This course was also taught as part of the Singapore-
fall-2003/
Differential
Equations
in Action
Calculus
One
Visualizing
Course
Udacity
Materials (Free)
– Enroll in
Course
($199/month aft
er 14-day trial)
Course
Ohio State
Materials
University
(Selfpaced online
course 25 hours of
videos and
quizzes)
Course
Udacity
https://ww
w.udacity.c
om/course/
cs222
https://ww
w.coursera.
org/course/
calc1
https://ww
MIT Alliance (SMA) programme as course number SMA
5509 (Theory of Parallel Systems).
In this course you will examine real world problems -rescue the Apollo 13 astronauts, stop the spread of
epidemics, and fight forest fires -- involving differential
equations and figure out how to solve them using
numerical methods.
Calculus is about the very large, the very small, and
how things change. The surprise is that something
seemingly so abstract ends up explaining the real
world. Calculus plays a starring role in the biological,
physical, and social sciences. By focusing outside of the
classroom, we will see examples of calculus appearing
in daily life.
This course is a first and friendly introduction to
calculus, suitable for someone who has never seen the
subject before, or for someone who has seen some
calculus but wants to review the concepts and practice
applying those concepts to solve problems. One learns
calculus by doing calculus, and so this course
encourages you to participate by providing you with:
- instant feedback on practice problems
- interactive graphs and games for you to play
- calculus projects and demos you can try at home
- opportunities for you to explain your thought process
Throughout this course, we will use algebra to quantify
Algebra
Materials
w.udacity.c
om/course/
ma006
Web
Developme
nt
Course
Udacity
Materials (Free)
– Enroll in
Course
($199/month aft
er 14-day trial)
Course
Udacity
Materials
https://ww
w.udacity.c
om/course/
cs253
How to
Build a
Startup
Course
Materials
Udacity
https://ww
w.udacity.c
om/course/
ep245
Startup
Course
Materials
Stanford
http://blake
masters.co
m/peter-
HTML5
Game
Developme
nt
https://ww
w.udacity.c
om/course/
cs255
and describe the world around us. Have you ever
wondered how many songs can fit onto your flash
drive? By the end of the course, you’ll have stronger
skills for modeling problems, analyzing patterns, and
using algebra to arrive at conclusions.
Starting from the basics of how the web works, this
class will walk you through everything you need to
know to build your own blog application and scale it to
support large numbers of users.
This course will walk you through the major
components of building GRITS, an HTML5 game. We'll
talk about how to take standard game development
techniques, and use them to create high performance
HTML5 applications.
Learn the key tools and steps to build a successful
startup (or at least reduce the risk of failure). An
introduction to the basics of Steve Blank's famous
Customer Development process, where entrepreneurs
"get out of the building" to gather massive amounts of
customer and marketplace feedback, and then use that
feedback to continuously iterate and evolve their
startup business models, improving the chances of
success at every step.
Organizatio Course
nal Analysis Materials
Stanford
University
thielscs183startup
https://ww
w.coursera.
org/course/
organalysis
It is hard to imagine living in modern society without
participating in or interacting with organizations. The
ubiquity and variability of organizations means there is
ample room for complexity and confusion in the
organizational challenges we regularly face. Through
this course, students will consider cases describing
various organizational struggles: school systems and
politicians attempting to implement education reforms;
government administrators dealing with an
international crisis; technology firms trying to create a
company ethos that sustains worker commitment; and
even two universities trying to gain international
standing by performing a merger.
Each case is full of details and complexity. So how do
we make sense of organizations and the challenges
they face, let alone develop means of managing them
in desired directions? While every detail can matter,
some matter more than others. This is why we rely on
organizational theories -- to focus our attention and
draw out relevant features in a sensible way.
Through this course you will come to see that there is
nothing more practical than a good theory. Every week,
you’ll learn a different organizational theory, and it will
become a lens through which you can interpret
concrete organizational situations. Armed with a
toolset of theories, you will then be able to
systematically identify important features of an
organization and the events transforming it – and use
the theories to predict which actions will best redirect
the organization in a desired direction.