Computer Network

I. Course Goal: STANFORD OCW – Prof. Nick McKeown, Philip Levis

Understand protocols, design of the Internet and principles of Computer Network

II. Course Topics:

Unit 1. The Internet and IP

Unit 2. Transport

Unit 3. Packet-Switching: mathematically heavy part

Unit 4. Congestion Control

————— Midterm Exam —————-

Unit 5. Applications and NATs

Unit 6. Routing

Unit 7. Lower Layers

Unit 8. Security

—————– Final Exam —————–

IV. Text Book:

Larry L. Peterson. Computer Networks: A Systems Approach

James F. Kurose. Computer Networks: Top-Down Approach

V. Plan:

∗ Each unit might take about 8 – 10 hours of week

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Linear Algebra

I. Course Overview: MIT OCW – Prof. Gilbert Strang

Linear algebra: branch of mathematics studying for linear equation system and matrix

 

II. Course Objective:

A. Systems of linear equation

B. Row reduction and echelon form

C. Matrix operation, including inverse

D. Block matrix

E. Linear Dependence and Independence

F. Subspace and bases and dimension

G. Orthogonal bases and Orthogonal projections

H. Gram-Schmidt process

I. Linear model and Lease square problem

J. Determinant

K. Cramer’s Rule

L. Eigenvalue and Eigenvector

M. Diagonalization

O. Symmetric Matrix

P. Positive definite matrix

Q. Similar matrix

R. Linear transformation

S. Singular Value Decomposition(SVD)

 

III. Prerequisite:

A. 18.02 Multi Variable Calculus

 

IV. Text Book:

Strang, Gilbert. Introduction to Linear Algebra. 5th ed

 

V. Plan:

∗ MIT students spend 150 hours for course, more than half for class and assignment

Probability Systems Analysis and Applied Probability

I. Course Overview: MIT OCW – Prof. John Tsitsiklis

Learn probabilistic modeling, random process and statistical inference

 

II. Course Objective:

A. Conceptual

1. probability model: basic concept

2. translation ability: model in words to mathematical ones

3. bayesian and classical inference: concept and assumption

4. applications of inference models

B. Technical

1. probability distributions

2. conditioning: simplify analysis of complex model

3. probability mass functions, densities and expectations

4. powers of laws of large numbers

5. conditional expectation: concept and role

6. Markov chain: formulate simple dynamic model

7. inference methodology: estimation and hypothesis testing

 

III. Prerequisite:

A. 18.01 Single Variable Calculus

B. 18.02 Multi Variable Calculus

 

IV. Why Study Probability?

To think probabilistically is now fundamental ability in science and engineering fields

i.e. quantum mechanics, communication and signal processing, social network

A. Increasing Complexity:

impossible to have perfect model of real world → uncertainty model needed

B. Abundant of Information:

huge information → apply probabilistic modeling and statistical inference must

 

V. Text Book:

Bertsekas, Dimitri, and John Tsitsiklis. Introduction to Probability. 2nd ed

 

VI. Plan:

∗ MIT students spend average 11-12 hours each week for lecture, reading and exams