IC2 – Introduction to Psychology

Lecturer: Katharina Krämer
Fields: Psychology / Developmental Psychology, Social Psychology, and Clinical Psychology

Content

This course is intended for all participants (psychologists and non-psychologists alike), who are curious about the human mind and its functions. And that is basically what psychology is about: Psychological research investigates the role of mental functions in individual and social behaviour and explores the physiological and biological processes that underlie cognitive functions and behaviours. As a social science, psychology aims to understand individuals and groups by establishing general principles and researching specific cases by using quantitative and qualitative research methods.

During this introduction to psychology we will get to know the major schools of thought, including behaviourism and cognitive psychology as well as psychoanalysis and psychodynamic psychology. Psychological research encompasses many subfields and includes different approaches to the study of mental processes and behaviour. In this course, we will focus in particular on the sub-disciplines of developmental psychology, social psychology, and clinical psychology. Thereby, we will explore the following questions:

  • How does the human mind develop through the live span? How do people come to perceive, understand, and act within the world? How do these processes change as people age?
  • How do humans think about each other? How do they relate to each other? What are the influences of others on an individual’s behaviour? How do people form beliefs, attitudes, and stereotypes about other people?
  • How and why do mental disorders develop? How can we prevent mental disorders and psychologically based distress? How can we promote subjective well-being and personal development?

Objectives

  • To get an overview of the different sub-disciplines of psychology and psychological research methods
  • To get a broad idea how the human mind works, how people function and what motivates and explains their behaviour
  • To understand how psychological knowledge can be applied to the assessment and treatment of mental health problems

Literature

As there are many excellent textbooks on psychology and its sub-disciplines, this is just a small selection if you want to do some background reading.

  • Aronson, E., Wilson, T.D. & Akert, R.M. (2013). Social Psychology (8th Edition). Pearson.
  • Barlow, D.H. (2014). The Oxford Handbook of Clinical Psychology. Oxford University Press.
  • Gerrig, R.J., Zimbardo, P., Svartdal, F., Brennan, T., Donaldson, R. & Archer, T. (2012). Psychology and Life (19th Edition). Pearson.
  • Slater, A. & Bremner, J.G. (2017). An Introduction to Developmental Psychology (3rd Edition). The British Psychological Society and Wiley.

Lecturer

Dr. Katharina Krämer
Dr. Katharina Krämer

Katharina Krämer is a psychologist and psychoanalytic psychotherapist. She works as a professor for psychology at the Rheinische Fachhochschule Köln, Germany, and as a psychotherapist at the Department of Psychiatry at the University Hospital Cologne, Germany. In 2014, Katharina Krämer received her doctoral degree from the University of Cologne, Germany, on a thesis investigating the perception of dynamic nonverbal cues in cross-cultural psychology and high-functioning autism. She works with patients with different mental disorders, focusing on adult patients with autism. Her research interests include the application of Mentaliszation-Based Group-Therapy with patients with autism and the vocational integration of patients with autism.

Affiliation: Rheinische Fachhochschule Köln
Website: https://www.rfh-koeln.de/studium/studiengaenge/wirtschaft-recht/wirtschaftspsychologie/dozenten/katharina_kraemer/index_ger.html

IC1 – Introduction to Machine Learning

Lecturer: Benjamin Paassen
Fields: Artificial Intelligence / Machine Learning

Content

This course is intended for non-machine learners with little to no prior knowledge. It will provide many examples as well as accompanying exercises and limit the number of formulae to a bare minimum, while instead maximizing the number of meaningful images. In more detail, the course will cover the following topics.

  • Session 1: Basics of optimization (What is a mathematical optimization problem? How do we model the world in optimization? How do we solve optimization problems?), basics of probability theory (What are distributions, joint and conditional probabilities, and Bayes’ rule? How do we maximize probabilities?), and linear regression from a geometric and a risk minimization perspective
  • Session 2: Machine learning from the perspective of distances for classification (how do I put things into known categories?), clustering (how do I discover new categories of things?), regression (how do I infer an unknown variable from a known one, based on examples?), and dimensionality reduction (how do I simplify data that is too big to process?)
  • Session 3: Neural-network-based learning (What is an artificial neural network? What are popular components? What kind of models can I build? How do I learn such models?) and the problems of generalization (When can learning fail? How do I prevent that? How can hackers attack my model?)
  • Session 4: Reinforcement learning (What is it and how do I do it?) and algorithmic fairness (What does it mean to be fair and what role to risk, reward, and curiosity play?)

Objectives

  • becoming familiar with key concepts from machine learning (e.g. risk minimization, exploration versus exploitation, priors and posteriors, generalization)
  • achieving a high-level understanding of how the most popular machine learning methods work and which method can be used for which application (e.g. when not to use deep learning methods)
  • de-mystifying machine learning (it is just a collection of methods with certain assumptions)
  • optionally, becoming able to apply some machine learning methods in Python on your own data (exercises)

Literature

Lecturer

Benjamin Paassen
Dr. Benjamin Paassen

Benjamin Paassen received their doctoral degree in 2019 from Bielefeld University, Germany on the topic of ‘Metric Learning for Structured Data’. Prior work has focused on machine learning algorithms to support applications in computer science education and hand prosthesis research, but has also included research on discrimination in video game culture and in machine learning. Research interests include interpretable machine learning, metric learning, transfer learning, and fairness.

Affiliation: Bielefeld University and University of Sydney
Website: https://bpaassen.gitlab.io/