G. Elective Courses

G.1. Analytical Methods (2 credits)

Lectures Exercise Laboratory Field Trip
2   

Discussions will focus on the theoretical basis of various analytical methods and will present practical examples.

The course will also include experimentation using the following methods and instruments:

  • Chromatography: ion chromatography, gas chromatography, HPLC.
  • Electrophoresis, scintillation counting, ultracentrifugation.
  • Spectrophotometry.

Lecturer: Z. Cohen

Recommended Reading: Provided during the course.


G.2. Reading Tutorial (1/2 credit)

Lectures Exercise Laboratory Field Trip
 1  

Lecturers: All faculty Recommended Reading: Provided during the course.


G.3. Student Seminar (1/2 credit)

Lectures Exercise Laboratory Field Trip
2   

Each student is required to attend student seminars and to present at least one seminar a year.

Lecturers: All students and guest lecturers

Recommended Reading: Provided during the course.


G.4. Solution Methods in Mathematics (3 credits)

Prerequisite:It is assumed that the students are familiar with the basic concepts, which will be reviewed

Lectures Exercise Laboratory Field Trip
3      

  • Linear equations (Matrices, vectors, eigenvalues, determinants; Examples -- linear regression and other problems)
  • Ordinary differential equations (Derivatives, integration, exponential functions, oscillatory motion, logistic equation, examples from population ecology)
  • Partial differential equations (Functions of several variables, partial derivatives, examples: Diffusion problems in biology).

Lecturer: Y. Zarmi

Recommended Reading: Provided during the course.


G.5. Design of Experiments and Statistical Analysis (3 credits)

Lectures Exercise Laboratory Field Trip
2 1    

  1. Basic concepts of statistics.

  2. Experiment to compare two treatments.
  3. One-way analysis of variance (ANOVA).
  4. The variance and its sources in experiments
  5. Two-way ANOVA: Various models.
  6. Comparison between experimental treatments.
  7. Multi-factorial experiments.
  8. Split-plots experiments.
  9. Factors affecting size and shape of plots and the number of replicates.
  10. Sampling methods.
  11. Experiments with large number of treatments.

Lecturer: J.E. Ephrath, M. Silberbush

Recommended Reading:
Roger, G.P. (1994). Agricultural Field Experiments: Design and Analysis. Marcel Dekker Inc.
Mead, R., R.N. Curnow and A.M. Hasted. (1993). Statistical Methods in Agriculture and Experimental Biology.2nd Edition. Chapman and Hall.
Rholf, F.J. and R.R. Sokal. (1981). Statistical Tables.2nd Edition. W.H. Freeman and Co. New-York.


G.6. Summarizing, Writing and Presenting Scientific Data (2 credits)

Lectures Exercise Laboratory Field Trip
1 2    

This course includes:

  • Preparation of tables and figures, writing legends and titles of figures and tables.
  • Preparation of a scientific seminar.
  • Preparation of poster.
  • Principles of scientific writing.

The course consists on 1 weekly hour lecture + 2 weekly hours exercise. It is also includes weekly assignment and final project.

Lecturer: A. Vonshak

Recommended Reading: Provided during the course.


G.7. Statistical Methods (3 credits)

Pre-requisite: Basic knowledge of calculus and linear algebra.

Lectures Exercise Laboratory Field Trip
3      

The course includes:

  • Descriptive Statistics: mean, mode, median, range, quantiles, standard deviation, variance, frequency curve, cumulative frequency curve.
  • Event Space: probability, simple and compound events, elementary set operations, probability axioms, conditional probability, Bayes Principle, dependence and independence.
  • Random Variables: discrete and continuous variables, density, cumulative distribution, expectation, variance, moments, Markov and Chebyshev's inequalities, moment generating function.
  • Jointly Distributed Variables: joint distributions, marginal distributions, conditional distributions, joint variance, correlation coefficient, sums of random variables, the (weak) Law of Large Numbers.
  • Discrete Distributions: Uniform, Binomial, Geometric, Hypergeometric and Poisson distributions.
  • Continuous Distributions: Uniform and Exponential distribution, memoryless property, hazard rate, Weibull, Gamma and Normal distributions, Central Limit Theorem.
  • Statistical Inference: Estimation: Populations and samples, parameters and statistics, unbiased estimators, sample distributions for the mean, variance and proportion, empirical distribution, confidence interval, confidence level, sample size, degrees of freedom, t- and X2 distributions.
  • Statistical Tests - Hypothesis Testing: the Null and Alternative hypotheses, significance level, acceptance and rejection regions, critical values, errors of the first and second type, power, goodness of fit, hypotheses on different samples, the F distribution.
  • Regression: relations among variables: statistical relation, causal relation, dependence and independence, index of fit, explained variance, regression line, regression coefficients estimators.
  • Multivariate Regression: The Method of Least Squares, regression coefficients, covariance matrix.

Lecturer: A. Zemel

Recommended Reading: Provided during the course.