Math 2: General Course Outline
Course Description
2. Finite Mathematics. (4)Lecture, three hours; discussion, one hour. Preparation: three years of high school mathematics. Finite mathematics consisting of matrices, Gauss/Jordan method, combinatorics, probability, Bayes theorem, and Markov chains. P/NP or letter grading.
Course Information:
The following schedule, with textbook sections and topics, is based on 23 lectures. The remaining classroom meetings are for leeway, reviews, and midterm exams. These are scheduled by the individual instructor. Often there are reviews and two midterm exams about the beginning of the fourth and eighth weeks of instruction, plus reviews for the final exam.
Math 2 may be used to satisfy the quantitative reasoning requirement of the College of Letters and Science.
The main topic of Math 2 is the theory of probability. This subject is important for many of the applications of mathematics to other areas. Many facets of everyday life involve probabilities, as TV program ratings, insurance rates, freethrow shooting percentages, birthrates, inherited traits, and the California lottery.
Probability furnishes the mathematical foundations for statistics. Consequently, Math 2 complements the courses in statistics taken by many of the students majoring in the social and biological sciences. Math 2 may be taken either before or after an introductory course in statistics.
Textbook(s)
R. Brown, and B. Brown, Essentials of Finite Mathematics: Matrices, Linear Programming, Probability, Markov Chains, Ardsley House.
Outline update: P. Greene, 11/12
Schedule of Lectures
Lecture  Section  Topics 

1 
3.1 
Introduction: Probability and Odds 
2 
3.2 
Counting 
3 
3.3 
Permutations and Factorials 
4 
3.4 
Combinations 
5 
3.5 
Computing Probability by Counting 
6 
3.63.7 
Union of Events, Disjoint Events 
7 
3.8 
Conditional Probability 
8 
3.9 
Intersection of Events 
9 
4.1 
Partitions 
10 
4.2 
Bayes? Theorem 
11 
4.3 
Random Variables and Probability Distributions 
12 
4.4 
Expected Value and Variance 
13 
4.5 
Binomial Experiments 
14 
4.6 
The Normal Distribution 
15 
4.7 
Normal Approximations for the Binomial Distribution 
16 
More Practice with Binomial Distributions 

17 
1.1 
Matrices 
18 
1.2 
Matrix Multiplication 
1920 
1.4 
Solving Linear Systems using Gauss Jordon Method 
21 
5.1 
Matrices and Probability 
22 
5.2 
Markov Chain Processes 
2324 
5.3 
Equilibrium requiring Gauss?Jordon 