Learning Outcomes: 1. Mathematically represent common single variable discrete and continuous probability functions that are relevant in actuarial applications such as Poisson, binomial, negative binomial, and geometric, uniform, exponential, and normal.
Audience: Undergraduate

2. Calculate measures related to the characteristics of single variable probability functions such as moments, mean, variance, median, mode, percentiles, etc., and interpret them in an actuarial context.
Audience: Undergraduate

3. Derive joint, marginal and conditional probabilities and densities from multivariate distributions and calculate covariance and correlation coefficients, and interpret them in an actuarial context.
Audience: Undergraduate

Learning Outcomes: 1. Illustrate the impact of the time value of money on decisions both in their professional and personal life.
Audience: Undergraduate

2. Compare and contrast the dynamics of key financial instruments including annuities, bonds and loans as well as calculate the price and other significant values of these instruments.
Audience: Undergraduate

3. Demonstrate the importance of immunizing asset and liability portfolios against changes in interest rates and describe methods of immunization.
Audience: Undergraduate

4. Explain the determinants of interest rates.
Audience: Undergraduate

5. Differentiate between the different types of measures of rate of return and return on investment and calculate those rates given a set of cash flow assumptions.
Audience: Undergraduate

6. Calculate the value of interest rate swaps.
Audience: Undergraduate

Learning Outcomes: 1. Understand and illustrate the impact of the time value of money on decisions both in their professional and personal life.
Audience: Undergraduate

2. Differentiate between the different types of measures of rate of return and return on investment and calculate those rates given a set of cash flow assumptions.
Audience: Undergraduate

3. Compare and contrast the dynamics of key financial instruments including annuities, bonds and loans as well as calculate the price and other significant values of these instruments.
Audience: Undergraduate

4. Demonstrate the importance of immunizing asset and liability portfolios against changes in interest rates and describe methods of immunization.
Audience: Undergraduate

Learning Outcomes: 1. Students will be able to communicate basic knowledge of content, analyze related data and apply knowledge in a variety of academic settings.
Audience: Undergraduate

Learning Outcomes: 1. Articulate key assumptions pertinent to various statistical methods for risk modeling in insurance.
Audience: Both Grad & Undergrad

2. Evaluate actuarial models for risk analysis using graphical procedures, hypothesis tests, and score-based approaches.
Audience: Both Grad & Undergrad

3. Demonstrate an understanding of the bias-variance trade-off within the actuarial and risk modeling context and its impact on model selection.
Audience: Both Grad & Undergrad

4. Employ resampling techniques for validating models used in risk assessment and management.
Audience: Both Grad & Undergrad

5. Explain and interpret statistical package outputs to risk managers and other stakeholders.
Audience: Both Grad & Undergrad

6. Apply supervised and unsupervised learning methods to address challenges in insurance applications.
Audience: Graduate

Learning Outcomes: 1. Describe the key features of life and annuity insurance.
Audience: Both Grad & Undergrad

2. Apply key concepts concerning parametric and non-parametric mortality models for individual lives.
Audience: Both Grad & Undergrad

3. Estimate parameters for parametric and nonparametric survival models for actuarial applications.
Audience: Both Grad & Undergrad

4. Describe and evaluate present value random variables associated with benefits and expenses for life and annuity insurance
Audience: Both Grad & Undergrad

5. Describe and use the premium and policy value calculation processes for life and annuity insurance
Audience: Both Grad & Undergrad

6. Apply long-term actuarial modeling tools in conjunction with other techniques to address challenges in insurance applications.
Audience: Graduate

Learning Outcomes: 1. Develop and utilize multiple state models in actuarial applications and calculate premiums and reserves for policies associated with multiple state models.
Audience: Undergraduate

2. Apply special cases of multiple state model in actuarial practice, including the multiple decrement and multiple life models.
Audience: Undergraduate

3. Understand liabilities and required contributions for pension plans and post retirement health plans.
Audience: Undergraduate

4. Evaluate premiums and liabilities for Universal Life insurance policies.
Audience: Undergraduate

5. Apply profit testing methodology to price and evaluate insurance policies.
Audience: Undergraduate

Learning Outcomes: 1. Demonstrate the appropriateness of alternative probability models, for both frequency and severity, when applied to short-term insurance loss data.
Audience: Both Grad & Undergrad

2. Evaluate the effects of coverage modifications on severity models and adjust the models to accommodate the coverage change.
Audience: Both Grad & Undergrad

3. Explain the model building process, and estimation, for the analysis of short-term insurance loss data.
Audience: Both Grad & Undergrad

4. Build aggregate loss models that quantify the risks of an insurance system, and utilize these models in applications such as reinsurance.
Audience: Both Grad & Undergrad

5. Interpret the concepts of ratemaking and reserving, and prepare data for short-term insurance ratemaking and reserving analyses.
Audience: Both Grad & Undergrad

6. Apply short-term actuarial modeling tools in conjunction with other techniques to address challenges in insurance applications.
Audience: Graduate

Learning Outcomes: 1. Create new severity distributions through techniques including random variable transformation, mixtures, and splicing.
Audience: Undergraduate

2. Apply extreme value distributions to the estimation of tail measures and probabilities in short-term insurance applications.
Audience: Undergraduate

3. Perform model selection using graphical procedures, hypothesis tests, and score-based approaches with a focus on short-term insurance applications.
Audience: Undergraduate

4. Estimate losses for short-term insurance coverages using credibility procedures including greatest accuracy credibility and empirical Bayesian estimation.
Audience: Undergraduate

5. Apply advanced techniques for estimating outstanding claims associated with short-term insurance coverages, and understand the underlying statistical models and assumptions.
Audience: Undergraduate

6. Apply advanced techniques to calculate premiums for short-term insurance coverages, such as calculating risk classification differential changes.
Audience: Undergraduate

Learning Outcomes: 1. Complete hands-on tutorials and exercises using linear regression and time series models relevant for actuarial practice.
Audience: Undergraduate

2. Utilize statistical software to estimate model parameters for regression models in business applications.
Audience: Undergraduate

3. Explain the importance of checking model assumptions and conducting residual analyses in business applications.
Audience: Undergraduate

4. Explain the impact of choices of explanatory variables and incorporate these variables in the model.
Audience: Undergraduate

5. Integrate regression concepts to other actuarial science courses including loss models, actuarial mathematics, and use of simulation.
Audience: Undergraduate

Learning Outcomes: 1. Discuss an assortment of issues in the health area to gain knowledge of the interaction of health, health care and health insurance.
Audience: Undergraduate

2. Synthesize research in assigned articles and critically discuss health-related peer-reviewed articles in a small, interactive group setting.
Audience: Undergraduate

3. Apply and extend knowledge in statistics and statistical modeling as related to health topics.
Audience: Undergraduate

4. Apply statistical methods to data from the Medical Expenditure Panel Survey (MEPS).
Audience: Undergraduate

5. Develop presentation and writing skills by critically discussing and analyzing a health analytics paper using MEPS data.
Audience: Undergraduate

Learning Outcomes: 1. Articulate the difference between point prediction and probabilistic prediction in a business context.
Audience: Undergraduate

2. Build predictive models for the purpose of probabilistic forecasting in business applications.
Audience: Undergraduate

3. Select appropriate modeling strategy and evaluate performance of the models in a given business context.
Audience: Undergraduate

4. Perform feature engineering to incorporate complex pattern and unstructured data into the predictive model development for business applications.
Audience: Undergraduate

5. Interpret the prediction outcome and communicate results to non-technical business audiences
Audience: Undergraduate