| Lecture | Section | Topic |
|---|
| 1 | 9.1 | Linear Systems |
| 2 | 9.2 | Matrices |
| 9.2.1 | Basic Matrix Operations |
| 9.2.2 | Matrix Multiplication |
| 3 | 9.2.3 | Inverse Matrices |
| 4 | 9.3 | Linear Maps, Eigenvectors, and
Eigenvalues |
| 9.3.1 | Graphical Representations |
| 5 | 9.3.2 | Eigenvalues and Eigenvectors |
| 6 | 7.2 | Equilibria and Their Stability |
| 7.2.1 | A First Look at Stability |
| 7 | 7.2.2 | Single Compartment or Pool |
| 7.3.2 | A Compartment Model |
| 8 | 11.1 | Linear Systems -- Theory |
| 11.1.1 | The Direction Field |
| 11.1.2 | Solving Linear Systems |
| 9 | 11.1.3 | Equilibria and Stability |
| 10 | 11.2 | Linear Systems -- Applications |
| 11.2.1 | Compartment Models |
| 11 | | Review |
| 12 | | Midterm 1 |
| 13 | 11.3 | Nonlinear Autonomous Systems --
Theory |
| 11.3.1 | Analytical Approach |
| 11.3.2 | Graphical Approach for 2x2 Systems |
| 14 | 11.4 | Nonlinear Autonomous Systems --
Applications | |
| 11.4.1 | The Lotka-Volterra Model for
Interspecific Competition | |
| 15 | 12.1 | Counting |
| 16 | 12.2 | What is Probability? |
| 12.2.1 | Basic Definitions |
| 17 | 12.2.2 | Equally Likely Outcomes |
| 18 | 12.3 | Conditional Probability and
Independence |
| 12.3.1 | Conditional Probability |
| 12.3.2 | The Law of Total Probability |
| 19 | 12.3.3 | Independence |
| 12.3.4 | Bayes Formula |
| 20-21 | 12.4 | Discrete Random Variables and
Discrete Distributions |
| 12.4.1 | Discrete Distributions |
| 12.4.2 | Mean and Variance |
| 12.4.3 | The Binomial Distribution |
| 22 | 12.5 | Continuous Distributions |
| 12.5.1 | Density Functions |
| 23 | | Review |
| 24 | | Midterm 2 |
| 25 | 12.5.2 | The Normal Distribution |
| 26 | 12.5.3 | The Uniform Distribution |
| 12.5.4 | The Exponential Distribution |
| 27 | 12.6 | Statistical Tools |
| 12.6.1 | Collecting and Describing Data |
| 28 | 12.6.2 | Estimating Means and Proportions |
| 29 | | Review |