Jeff Edmonds - Research Interests and Academic Courses

 
Jeff Edmonds
  room: 3044
  jeff@cs.yorku.ca
 
 
Many Topics in Theory & Mathematics
Scheduling Algorithms
scheduling some shared resource
to a steady stream of incoming jobs
Examples
scheduling jobs on multi-processor machine
regulating the flow of data through a network (TCP)
broadcasting files
Lower Bounds
Greedy/Dynamic Programming model.
Cake Cutting (Resource Allocation)
upper and lower bounds on the # of operations required
Topological Embeddings
Jeff Edmonds 
Research Interests
 
Research Interests
Y
X
f(X,Y)
 
Mathematical and Theoretical Support
For your favorite topic.
Jeff Edmonds 
 
Machine Learning
 
I had a life crisis this year.
I am tired of doing research that is
too hard and too esoteric.
So I have been studying machine learning.
 
Machine Learning
 
Machine learning:
Is changing our lives at a rate like never before.
            For better or worse
It is where the jobs are.
           The few that will be left.
York is starting a whole new grad program in it.
 
Machine Learning
 
I got addicted to making slides and it may be 4 hours
worth.
I want to do is slow so that everyone gets it.
I taught about 4 hours in EECS2001
and 1 hour in EECS101
And 1 hour later this month to BMO CEOs.
COSC6111
Advanced Algorithms
Design and Analysis
 
Description:
An advanced theory course (You need one)
Directed at non-theory students
Exposes you to many theory topics
Challenging, but accessible
Jeff Edmonds 
 
office hour??
   After class or before?
COSC 3101
Design and Analysis of Algorithms
 
Videos of my lectures
are all on line.
 
Think about attending it
    I find most grad students
    do not know this material.
Prerequisites
 
You should know the 
3101 material
 to take
this advanced graduate course in algorithms.
Existential and Universal Quantifier
Sums and Recurrence relation
Loop Invariants
Recursive Algorithms
Network Flow
Greedy Algorithms
Dynamic Programming
NP-Completeness
Prerequisites
 
You should know the 
3101 material
 to take
this advanced graduate course in algorithms.
We will spend much less time reviewing
and I will be more insistent that you know it.
Recommend that you
read my 3101 notes & slides
watch the videos.
Grading
 
Assignments              (30%)
Presentation              (30%)
Tests/Exam               (30%)
Class Participation    (10%)
 
Topics
 
Loop Inv: Maximal Rectangles
Divide and Conquer: fast fourier transformations
Recursion: parsing
Network Flow: steepest assent, bipartite matching matching
Linear Programming: what to put in a hotdog
Greedy Algorithms: matroids, union of matroids
Dynamic Programming: point cover, knapsack, parsing CFG
Approximation Algorithms: knapsack
Linear Algebra (FFT)
Lower bounds: In Backtracking model.
NP-completeness: reductions
Randomized Algorithms: chernoff bounds, primes, random walks
Cryptography: RSA
Distributed Systems: mud on forehead & common knowledge
# of prime numbers
Intro to Quantum: Shor's factoring
Amortized Analysis: union find
 
Jeff Edmonds
  room: 3044
  jeff@cs.yorku.ca
 
The Talk
 
Being able to give a good talk is an
important and difficult skill.
In the course evaluation, almost everyone
said that giving a talk was very useful, but
that hearing them was a big waist of time
because no one followed them.
 
The Talk
 
Grade
Class understanding and interest   33 1/3%
(marked by class)
Quality of material covered           33 1/3%
(relevancy, difficulty)
Quality of talk & slides                  33 1/3%
You will loose 3% for every minute over 20
mins. (We need a time keeper)
 
Book your date early
Discuss with me the topic
Two week before talk show me the slides
 
The Talk
Slide Note
Embed
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Jeff Edmonds is a researcher with interests in various theoretical and mathematical topics, including scheduling algorithms, cake cutting, and topological embeddings. He also provides support for mathematical and theoretical topics. Additionally, Jeff has ventured into machine learning to explore new horizons. He teaches advanced algorithms design and analysis courses like COSC6111 and COSC3101. His dedication to educating others shines through his commitment to creating educational materials and delivering lectures.

  • Jeff Edmonds
  • Research Interests
  • Scheduling Algorithms
  • Machine Learning
  • Academic Courses

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  1. Jeff Edmonds room: 3044 jeff@cs.yorku.ca

  2. Research Interests Many Topics in Theory & Mathematics Scheduling Algorithms scheduling some shared resource to a steady stream of incoming jobs Examples scheduling jobs on multi-processor machine regulating the flow of data through a network (TCP) broadcasting files Lower Bounds Greedy/Dynamic Programming model. Cake Cutting (Resource Allocation) upper and lower bounds on the # of operations required Topological Embeddings Jeff Edmonds

  3. Research Interests X Y f(X,Y)

  4. Mathematical and Theoretical Support For your favorite topic. Jeff Edmonds

  5. Machine Learning I had a life crisis this year. I am tired of doing research that is too hard and too esoteric. So I have been studying machine learning.

  6. Machine Learning Machine learning: Is changing our lives at a rate like never before. For better or worse It is where the jobs are. The few that will be left. York is starting a whole new grad program in it. w1 w2 x1 cat dog face pixeli,j bicycle. wm E(w1, ,wm) xn

  7. Machine Learning I got addicted to making slides and it may be 4 hours worth. I want to do is slow so that everyone gets it. I taught about 4 hours in EECS2001 and 1 hour in EECS101 And 1 hour later this month to BMO CEOs. w1 w2 x1 cat dog face pixeli,j bicycle. wm E(w1, ,wm) xn

  8. COSC6111 Advanced Algorithms Design and Analysis Description: An advanced theory course (You need one) Directed at non-theory students Exposes you to many theory topics Challenging, but accessible office hour?? After class or before? Jeff Edmonds

  9. COSC 3101 Design and Analysis of Algorithms Think about attending it I find most grad students do not know this material. Videos of my lectures are all on line.

  10. Prerequisites You should know the 3101 material to take this advanced graduate course in algorithms. Existential and Universal Quantifier Sums and Recurrence relation Loop Invariants Recursive Algorithms Network Flow Greedy Algorithms Dynamic Programming NP-Completeness

  11. Prerequisites You should know the 3101 material to take this advanced graduate course in algorithms. We will spend much less time reviewing and I will be more insistent that you know it. Recommend that you read my 3101 notes & slides watch the videos.

  12. Grading Assignments (30%) Presentation (30%) Tests/Exam (30%) Class Participation (10%)

  13. Topics Loop Inv: Maximal Rectangles Divide and Conquer: fast fourier transformations Recursion: parsing Network Flow: steepest assent, bipartite matching matching Linear Programming: what to put in a hotdog Greedy Algorithms: matroids, union of matroids Dynamic Programming: point cover, knapsack, parsing CFG Approximation Algorithms: knapsack Linear Algebra (FFT) Lower bounds: In Backtracking model. NP-completeness: reductions Randomized Algorithms: chernoff bounds, primes, random walks Cryptography: RSA Distributed Systems: mud on forehead & common knowledge # of prime numbers Intro to Quantum: Shor's factoring Amortized Analysis: union find

  14. Jeff Edmonds room: 3044 jeff@cs.yorku.ca

  15. The Talk Being able to give a good talk is an important and difficult skill. In the course evaluation, almost everyone said that giving a talk was very useful, but that hearing them was a big waist of time because no one followed them.

  16. The Talk Grade Class understanding and interest 33 1/3% (marked by class) Quality of material covered 33 1/3% (relevancy, difficulty) Quality of talk & slides 33 1/3% You will loose 3% for every minute over 20 mins. (We need a time keeper)

  17. The Talk Book your date early Discuss with me the topic Two week before talk show me the slides

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