Mellon College of Science - Teaching Excellence & Educational Innovation - Carnegie Mellon University

MCS Learning Objectives Samples

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09-341 The Art and Science of Color (Catalina Achim)

At the end of the class, students should be able to:

  • Relate artistic effects created by using pigments, changes in the aspects of artworks over time, and restoration methods to physical and chemical properties of inorganic pigments.
  • Know the characteristics of, and take practical advantage of, the properties of a wide range of artist’s pigments.
  • Use and read different color measuring systems and understand the relationships between them.
  • Be able to make and use egg tempera paints in a variety of ways
  • Communicate and collaborate effectively across the art & chemistry disciplines.

21-241 Matrix Algebra (Andrew Beveridge)

By the conclusion of this course, you are expected to have gained the ability to:

  • Solve problems using matrix techniques and algorithms.
  • Recognize and recall major linear algebraic definitions and theorems.
  • Develop short but rigorous proofs of true mathematical statements and construct counterexamples for false statements.
  • Apply major linear algebraic theorems to prove other results.
  • Interpret linear algebra techniques and results as geometric operations and structures in 3-dimensional space.

09-109 Kitchen Chemistry (Subha Das)

By the conclusion of this course, you are expected to have gained the ability to:

  • Identify the key chemical features and characteristics of basic food ingredients and explain how these properties affect and determine their handling, use, and taste.
  • Analyze and compare ingredients, recipes and protocols and be able to predict the purpose of and test the effectiveness of ingredients in recipes and dishes.
  • Use scientific principles to produce dishes using novel techniques and explain how they were constructed.
  • Exemplify the scientific process and inquiry driven research by designing, implementing, and documenting experiments.
  • Work effectively in a laboratory groups.

21-256 Multivariate Analysis and Approximation (Daniela Mihai)

By the conclusion of this course, you are expected to have gained the ability to:

  • Solve mathematical models (given as such or obtained from real life problems) by using multivariate analysis techniques:  vector operations, matrix operations, integration in various coordinate systems, optimization methods.
  • Interpret and analyze the results in the context of the problem (for example, what is the significance of the value of the Lagrange multiplier in an optimization problem, does a negative value make sense for an area.)
  • Formulate real life problems (word problems) into mathematical language, and solve them by using multivariate analysis techniques.
  • Recognize situations in which multivariate analysis concepts can be applied to economics, biology, statistics (or other fields), and identify which concepts and techniques are necessary to solve a specific problem.