English

This introductory Python algorithms course is designed for beginners in Python programming. Conducted online, this course excludes difficult mathematics and complex code, allowing students to directly code and debug basic algorithms. The course will be conducted slowly and in detail to accommodate coding novices. 

This course examines the impact of digital technologies on our health and wellbeing and includes consideration of how these devices and software interact with the healthcare system, affect attitudes towards health and healthcare providers, and change the discussions about health ethics, and health equity. 

This course explores the concept and process of design thinking. Students will gain awareness of their own process, develop research skills and methods necessary for any design project, both in academia and in the commercial world, and examine methods for projecting plausible futures based on current trends. 

The course introduces the history and development of thoughts on design as a discipline and important concepts that have significantly contributed to design research and studies. Students will encounter curated design thoughts and assignments to help develop their own perspectives on design and produce useful/usable work pieces for their career.  

This course primarily focuses on economic analysis in terms of welfare. Topics include how to evaluate market allocations based on efficiency, how to achieve efficient allocations through the market, and when the market fails in achieving efficient allocations.  

Students will study market structures besides the competitive market such as standard monopoly (uniform pricing), monopoly behavior (price discrimination) and oligopoly (basic concepts in game theory are also covered).  

Additionally, the course will consider exchange, production, welfare, social choice (e.g., an investigation of voting rules), and externalities (If time allows).  

This course emphasizes the development of microeconomic models to analyze economic decision-making of agents and provides students with the basic toolkit of microeconomic theory in preparation for advanced further coursework.  

Prerequisites: Principles of Microeconomics, Basic calculus 

This course applies a critical lens to representations of gender and identity in contemporary media. Taking gender and sexuality as a critical starting point, students examine the construction of identities under the simultaneous influence of race, class, and nationality. By focusing on popular representations in both the US and the country where the course is taking place, students gain a deeper understanding of identities as both culturally specific and influenced by global media. Instead of suggesting that contemporary identities are determined by what is on TV screens, computers, and in local movie theaters, the course seeks to describe the complex interactions between national audiences and concrete media productions. It analyzes how different audiences reproduce or challenge traditional concepts and stereotypes of gender, race, sexuality, and class. By combining the study of theoretical texts with examples from the advertisement industry, television, movies, and other forms of contemporary cultural expression, it offers a comprehensive and thorough introduction to contemporary studies of the media and identity.

This course examines suitable nutrition to animals in order to obtain good animal health management. It covers estimating the nutritive value of feeds; estimating the nutrient requirements of animals and diet formulation. The focus is on building up knowledge on animal nutrition by assessments of nutritional adequacy and solving of nutritional problems, with a particular emphasis on wildlife and animals used in agricultural production systems.

This course examines introductory level scientific and engineering design concepts including sustainable development, and product and processing line management with an emphasis on sustainable manufacturing as the core theme throughout the course. A technical lecture series will demonstrate the integration of all aspects of food science and technology, and their underpinning by the basic sciences, through examination of a hypothetical company producing a selected food product.  

This course examines major topics in pattern recognition, particularly aspects of classification and decision. Students will gain effective pattern recognition tools with which to analyze the often vast amounts of diverse data in research applications. 

Topics include introduction to pattern recognition - machine perception - PR systems and design cycle, Bayesian decision theory for continuous features - Bayes Decision Rule - minimum-error-rate classification - classifiers, normal density, discriminant functions and discrete Bayesian decision theory - discriminant functions for the normal density - error probabilities and integrals - Bayes Decision Theory for discrete features, maximum-likelihood and Bayesian parameter estimation - Bayesian parameter estimation: Gaussian case - Bayesian parameter estimation: general theory - HMM, nonparametric techniques - density estimation - Parzen windows - nearest neighbor estimation (NN, k-NN) - fuzzy classification, linear discriminant functions i - linear discriminant functions and decision surfaces - generalized linear discriminant functions - minimizing the perceptron criterion function, relaxation procedures, linear discriminant functions ii - minimum square-error procedures - relation to Fishers linear discriminant - the Widrow-Hoff and Ho-Kashyap procedures - multicategory generalizations - ridge regression and its dual form [2] - classification error based method [2], model assessment and performance evaluation - bias, variance and model complexity [2] - model assessment and selection 2] - confusion matrix, error rates, and ROC [2] - statistical inference [2] - statistical errors [2], dimension reduction and feature extraction - principal component analysis - Fisher linear discriminant - nonlinear projections, support vector machines - introduction - SVM for pattern recognition [2] - linear support vector machines [2] - nonlinear support vector machines [2], multilayer neural networks - introduction - feedforward operation and classification - backpropagation algorithm - some issues in training neural networks - key ideas in classification, introduction to deep learning networks - convolutional neural networks (CNN) - autoencoders - deep belief networks - deep reinforcement learning - generative adversarial networks (GAN), algorithm-independent machine learning - introduction - bias and variance - resampling for classifier design - estimating and comparing classifiers - combining classifiers, unsupervised learning and clustering - mixture densities and identifiability - maximum-likelihood estimates - application to normal mixtures. 

Prerequisites: Linear Algebra, Probability, MATLAB, Python, or C-Programming Skills 

Mathematics is at the same time a conceptual framework, a collection of proven theorems, and a toolbox. In this course, students encounter all three of these aspects by studying one of the central mathematical issues for applications in science and engineering. The general topic of the course is the solution of linear partial differential equations using the separation of variables, Fourier series, and Fourier transforms. The study involves both computational and rigorous mathematical aspects. While the actual computation of solutions is the main objective, students also learn the mathematical theorems establishing the validity and limitation of the different methods. Interested students are also offered the possibility to experiment with numerical approaches.  In addition to the contact hours, each student is expected to work nine hours a week on the course. This time should be devoted to reviewing the material of the preceding lecture; finishing the exercises started in the preceding problem session; preparing exercises to hand in; studying the corrections of the previously returned hand-in problems and making sure everything is clear. Entry Requirements: Calculus and Linear Algebra.