Course material

Lectures:

• Lecture0: Preliminaries (From professor Blitzstein)

• Lecture1: About this course

• Lecture1': About this course (Cont'd)

• Lecture2: Definition of probability

• Lecture3: Conditional probability

• Lecture4: Discrete random variables, Distribution, Mean, and variance

• Lecture5: Continuous random variables.

• Lecture6: Joint and marginal distribution, notion of independence, weak law of large numbers, central limit theorem (note that this lecture is not complete). 

• Lecture7: Estimation, Method of Moments, Maximum Likelihood, Confidence Interval; Link1, Link2

Homeworks:

• HW0 ; Solution;

• HW1; Solution; 

• HW2; Solution;

• HW3; Solution; 

• HW4; Solution;

• HW5; Solution;

• HW6; Solution;

• HW7; Solution; 

Sample Exams:

• Sample midterm; Solution

• Sample Final; Solution

Exams

• Midterm exam; Solution; Histogram

• Final Exam; Solution

Links:

• Studying R programming language

• You are not comfortable with summation notation?

• You are not comfortable with integral notation?