Teaching PortfolioAlexander Jung

May 12, 2018

Figure 1. First lecture of the Aalto course “Machine Learning: Basic Principles” in Sept. 2017.

some insights after few years teaching experience:

“If you want to master something, teach it!” (Richard Feynman)

“It’s more efficient to ask for pardon afterwards, than to ask for permission beforehand” (a teacher colleague).

The right time to start preparing the next edition of a course is right after the last lecture of the current edition.

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CONTENTS

I Teaching experience including supervision of doctoral and master level theses 3

II Development of teaching, experience in course and curriculum development 4

III Teaching and learning materials 5

IV Teaching philosophy and approach to teaching 6

V Pedagogical education and studies 6

VI Experience in educational leadership 6

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I. TEACHING EXPERIENCE INCLUDING SUPERVISION OF DOCTORAL AND MASTER LEVEL THESES

I became aware of my passion for teaching quite early, maybe at the stage of high-school. I loved to explain difficult studysubject matter to my colleagues at high school, university and at work. I am quite grateful to end up on tenure track at Aaltouniversity and become the main teacher for the courses about machine learning and artificial intelligence, whose relevance tocurrent and future society is tremendous. Teaching those courses in front of hundreds of curious and motivated students is anextraordinary privilege but also an obligation to provide only the highest quality.

I have (co-)taught the following courses

• 2006: Bachelor level course “Fundamentals of Electrical Engineering” at TU Vienna (link to webpage)• 2008: Master/Phd level course “Distributed Signal Processing” at TU Vienna• 2013-2015: Bachelor level course “Signals and Systems 2” (link to webpage) at TU Vienna• 2014-2016: Master level course "Compressed Sensing" at TU Vienna link to webpage.• since 2015: Master level course "Machine Learning: Basic Principles" (link to webpage) at Aalto university; enrolled by

more than 600 students in 2017!• 2016,2017: Master/Phd level course “Convex Optimization for Big Data” (link to webpage) at Aalto university; enrolled

by more than 50 students in 2017!• since 2018: Bachelor/Master level course “Artificial Intelligence” (link to webpage) at Aalto university; enrolled by more

than 480 students in 2018!

I have (am) (co-)supervised (supervising)

• two bachelor students• more than ten master students• more than five Phd students• two Post-Docs.

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II. DEVELOPMENT OF TEACHING, EXPERIENCE IN COURSE AND CURRICULUM DEVELOPMENT

Right from my start at Aalto in the summer 2015, I started to teach the course “Machine Learning: Basic Principles” (MLBP).Given the time constraints, I had to mainly follow the course design of the previous year. However, throughout the next twoyears I have redesigned the course completely (see Figure 2). The most recent edition consisted of high-level lectures whichfocused on the main principles and ideas. The hard skills have been trained in the exercises, which included challenging homeassignments. One of the few aspects that has been continued from previous editions is the data analysis project. According tothe student feedback, the data analysis project supports the students’ learning in many ways. First, it has a game like elementby allowing students to compete against each other in solving a particular machine learning problem. Moreover, this projectrequires students to combine the different concepts taught in the lectures, thereby supporting deep learning.

Figure 2. Evolution of Aalto course CS-E3210 “MLBP”, as depicted using sample slides from the intro lecture, which I was handling from 2017 on.

At Aalto university, I have developed a new course on convex optimization from scratch. The first edition, which was run inspring 2016, emphasized implementation aspects, including big data frameworks. I have then redesigned the course completelyin its second edition, which was run in spring 2017. The idea of the second edition was to provide an in-depth introductioninto the theory of convex analysis and optimization. To this end I have also used different teaching methods, e.g., avoidinglengthy slide shows in favour of blackboard presentations (see Figure 3). The blackboard presentations have been capturedusing snapshots made available to the students. The students had the opportunity to earn points by providing simple slidesbased on the blackboard drawings. The idea was to reinforce students learning by asking them to explain the course content inthe form of slides. Another novelty in this course was to use peer grading, i.e., the students had to review each other’s solutionsto the home-assignments via the web-based software “peergrade.io” (link to webpage). According to the student feedback wereceived, peer grading can be a valuable tool for enhancing learning but it needs to be used carefully. In particular, the review

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guidelines need to be narrowed down sufficiently in order to cope with the diverse backgrounds of different students.

Figure 3. A snapshot of the blackboard drawings during a lecture of the course “CS-E4020 - Convex Optimization for Big Data over Networks” held inspring 2017.

Since 2018 I am also teaching the course CS-E4800 “Artificial Intelligence” (AI) jointly with Prof. Tomi Janhunen. My partof the course teaches the basic ideas behind Markov decision process (MDP) models and how they can be used to developcontrol software for AI applications (robots). For this one, I tried a new approach which is based on “story telling”: In orderto motivate the (rather abstract) theory of MDP, I used a cleaning robot (see Figure 4) as a running example which allowedto progressively develop all mathematical concepts required to understand modern reinforcement learning methods. The ideawas to somehow “materialize” abstract theoretical concepts by having a real cleaning robot (that I bought from “Gigangti”)moving around the big Aalto lecture hall Y202a (see Figure 1). The students seem to like this approach, as indicated by astudent feedback:

"The story of Roomba" material worked quite ok. Better than selected chapters from some book (like Russell Norvigbook in 2002).

III. TEACHING AND LEARNING MATERIALS

My preference is to use simple slides which help the students to get the basic ideas and concepts. The slides should keepaway the students from spending too much time taking handwritten notes, thereby allowing them to focus on the talk itself.Typically, I sketch only high-level ideas using slides with references to more comprehensive literature. In particular, a proof(sketch) is only given on the slides if it fits on one single slide.

Recently, I have started to appreciate the pleasures of using the blackboard or a tablet (there are lecture halls withoutblackboards !). It is a good tool to keep my own speed under control such that students can easier follow me along animportant derivation. However, I learnt that such derivations need to be prepared very well, taking into account the differentbackgrounds of student. When prepared and executed property students appreciate blackboard talks and even prefer them overslide shows.

In order to make references easily accessible for the students we try to make the material (papers, additional slides) availablevia the course page. Additionally I also provide links to video-lectures which seem to fit well into the concept of the course.If possible, a course should be based on some few excellent textbooks.

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Figure 4. The cleaning robot “Rumba”, which was a main actor of the course CS-E4800 “Artificial Intelligence”.

IV. TEACHING PHILOSOPHY AND APPROACH TO TEACHING

For a (future) researcher, it is mandatory to be fascinated by an idea, a problem or a concept, in order to produce exceptionalresearch results. Therefore, I am keen on providing the minimum necessary toolkit allowing students at the beginning of theirresearch career to develop a passion for a research field. As a concrete example, in our CS course, we pare down the theorypart to the bare bones to have sufficient time allowing the students to read and interpret current CS research papers on theirown. Of course, the right amount of fundamental concepts required by the students is difficult to determine. Therefore westrongly encourage students to give feedback, e.g., in the form of e-mails or by filling out survey sheets.

The slogan “to fan the flame” is also one of my paradigms for the supervision of master and PhD students. When discussingpossible research avenues with one of my students, I tried to convince the student that graph signal processing is cool by givingan example of a tiny social network where each node represents a person having a certain age. This group was well connected,i.e., a clique in mathematical terms. To each node, I assigned a number representing the age of the person. Intentionally, Ihypothesized that each person in the clique was about the same age, with the exception of one single person which was mucholder than the others. After writing this unusual age at the whiteboard, I immediately recognized the surprise of my student.That was the moment when I draw the students attention to the notion of smoothness of graph signals which is one of thecornerstones of the emerging field of graph signal processing.

On the other hand, my experience is that one should not fix too detailed work-plans for PhD students. Although a supervisorshould have a certain idea of what broad topic could yield interesting and novel results, it is extremely important for PhDstudents to have freedom and time to strive around in a wide field (e.g., graphical model selection or dictionary learning) and“smell the roses”. However, this freedom can be challenging and requires some amount of personal maturity and it is the dutyof the supervisor to take care that the student is not over-challenged.

V. PEDAGOGICAL EDUCATION AND STUDIES

I have completed the course “A!Peda Intro” in spring 2017 at Aalto university. Some of the key insights obtained duringthis course are

• it is crucial to maintain close contact with other teachers• students need to be trained in giving good (constructive) feedback• each single course must be considered as a part of a curriculum• different factors that cause students to experience high workload (uneven task distribution, many contact sessions, ...)

VI. EXPERIENCE IN EDUCATIONAL LEADERSHIP

I am quite active in exchanging ideas and experiences with my fellow teacher colleagues at the department and beyond.To this end, I have used my role in organizing the Professor Lunch (which has been changed to Faculty Lunch recently)

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seminar series in order to promote interaction between teachers. In particular, I have invited our pedagogics experts KirstiKeltikangas and Tomi Kauppinen as well as Prof. Jani Romanoff (“Dynamic Feedback and Work-life Skills Integration forMarine Technology M.Sc. Studies”) as guest speakers.

The collaboration with Prof. Tomi Janhunen within our joint AI course has taught me a lot about understanding the preferencesof Finish students. Regular meetings with Prof. Ville Kyrki, Prof. Mario Di Francesco, Prof. Otto Seppälä, Prof. Aris Gionis,Prof. Petteri Kaski and Prof. Jaakko Hollmen have widened my view on teaching as a joint effort of many teachers. I havealso learnt that educational leadership is quite an outstanding capability since it requires to find the right balance betweenmotivating and providing constructive criticism. My personal believe is that we need to listen more carefully to the students’opinions and see them as peers rather than “enemies” or potential “free-riders”.

We should treat students as first-class citizens of the scientific community and dedicate the same amount ofour attention to them as we dedicate to reviewers of publications or grant proposals.

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