Seit Januar 2017
Lehrer seit Januar 2017
Statistics: Introductory course at the Bachelor level
Von 48 Fr /Std
This course will be of interest to Bachelor students, who are struggling with their Statistics courses and need additional time to digest the basic concepts in Statistics. The purpose of this class would be to identify the level of the student, identify missing knowledge, set target level and gradually and systematically fill in the gaps.
Ort
Beim Schüler zu Hause :
- In der Umgebung von Lausanne, Schweiz
Über mich
I am a classical teacher: I do not use plenty of modern technology in my teaching. For the math, physics and statistics classes I consider it more than sufficient to rely on pen and paper. In my opinion, in those fields understanding comes from solving problems and discussing. My sessions usually consist of solving a few problems and practicing explaining the steps that we followed. I also give a small homework at the end of each session. I require that students do their homework prior to each session.
Bildung
B. Sc. in Physics (2010) - Jacobs University Bremen
M. Sc. in Physics (2013) - EPFL
PhD (ongoing) - UNIL (topic: Architectural process models of decision making)
M. Sc. in Physics (2013) - EPFL
PhD (ongoing) - UNIL (topic: Architectural process models of decision making)
Erfahrung / Qualifikationen
During my Master studies, I gave private lessons in Physics and Math to a high school student. During my PhD studies, I have taught tutorials and given lectures to Master-level statistics courses three years in a row.
Alter
Jugendliche (13-17 Jahre alt)
Erwachsene (18-64 Jahre alt)
Unterrichtsniveau
Anfänger
Mittel
Fortgeschritten
Dauer
45 Minuten
60 Minuten
90 Minuten
120 Minuten
Unterrichtet in
Englisch
Fachkenntnisse
Verfügbarkeit einer typischen Woche
(GMT -05:00)
New York
Beim Schüler zu Hause
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
The purpose of this class would be to identify the level of the student, identify missing knowledge, set target level and gradually and systematically fill in the gaps. This can be done in the following areas:
1) Mechanics (physics)
2) Electromagnetism (physics)
3) Atomic physics
4) Infinitesimal calculus: derivatives, integration, differential equation (math)
5) Probability theory (math)
1) Mechanics (physics)
2) Electromagnetism (physics)
3) Atomic physics
4) Infinitesimal calculus: derivatives, integration, differential equation (math)
5) Probability theory (math)
This class will teach highly specialized knowledge of how to construct cognitive models using the ACT-R cognitive architecture. It will consist in teaching basic Common Lisp (a programming language). We will then go through the tutorial of ACT-R, whereby we explain in detail how and why each of its components work. This class will be especially interesting to Bachelor and Master students in Psychology and Bachelor and Master students in Neuroscience interested in Cognitive Neuroscience.
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