Students

Report info

Report date	Tuesday, May 17, 2011 2:55:20 PM BST

Start date	Monday, April 4, 2011 12:47:00 AM BST

Stop date	Sunday, April 24, 2011 11:59:00 PM BST

Stored responses	248

Number of completed responses	194

Question 1

Do you conduct research in, teach, or study machine learning?

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Yes	241	97.18%	97.18%
No	7	2.82%	2.82%
Sum:	248	100%	100%
Total answered: 248

Question 2

What do you do?

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Graduate student	248	100%	100%
Sum:	248	100%	100%
Total answered: 248

Question 3

Which department(s) are you based in? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Biology	6	1.64%	2.42%
Cognitive science	7	1.92%	2.82%
Computer science	176	48.22%	70.97%
Electrical engineering	28	7.67%	11.29%
Engineering sciences	21	5.75%	8.47%
Machine Learning	73	20%	29.44%
Mathematics	13	3.56%	5.24%
Neuroscience	15	4.11%	6.05%
Physics	1	0.27%	0.4%
Psychology	1	0.27%	0.4%
Social science	1	0.27%	0.4%
Statistics	12	3.29%	4.84%
Other:	11	3.01%	4.44%
Sum:	365	100%	100%
Total answered: 248

Text input

MIT Media Lab

Applied Mathematics

Informatics

control and dynamical systems, robotics

signal processing

mechanical engineering

Empirical Inference

Computational Linguistics

Information systems

Language Technology

Mechanical and Chemical Engineering

Question 4

Which conferences do you regularly attend? (You may pick more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
AAAI	30	6.71%	18.52%
ACL	16	3.58%	9.88%
AISTATS	32	7.16%	19.75%
ALT	1	0.22%	0.62%
CogSci	3	0.67%	1.85%
COLT	8	1.79%	4.94%
CoNLL	4	0.89%	2.47%
Cosyne	7	1.57%	4.32%
CVPR	21	4.7%	12.96%
ECCV	11	2.46%	6.79%
ECML	30	6.71%	18.52%
EMNLP	13	2.91%	8.02%
ICCV	14	3.13%	8.64%
ICML	73	16.33%	45.06%
IJCAI	28	6.26%	17.28%
IROS	9	2.01%	5.56%
ISMB	3	0.67%	1.85%
NAACL	10	2.24%	6.17%
NIPS	93	20.81%	57.41%
RECOMB	3	0.67%	1.85%
SIGIR	10	2.24%	6.17%
SIGGRAPH	1	0.22%	0.62%
Snowbird	5	1.12%	3.09%
UAI	22	4.92%	13.58%
Sum:	447	100%	100%
Total answered: 162

Text input

CHI

no regular ones

non

OHBM

ACM Multimedia

International Conference on Inductive Logic Programming (ILP)

WWW, KDD

ISMIR

KDD

MLSP, ISIT

ICPR, MCS, ICISP

KDD, PKDD, ICDM, SDM

have just attended AISTATS

CHI, Ubicomp, Pervasive, IUI

CVPR
(clicked conferences where I have published, no regular attendance as a student)

KDD

www?kddEMNLP????

CDC

CoSyNe

Asian Conference On Machine Learning

KDD

RSS, NIPS, ICRA

FOCS, STOC

ICDAR
ICPR
GREC

I attended CICLING 2011, and plan to attend ACL 2011

ICRA, HRI

PRASA (local South African)

ICANN, ESANN, ECAI, ADPRL

Gecco, icann, agi, ppsn, cec

ESWC, ISWC

Dessert

I have a attended once ICML 2010

UBICOMP

CVPR, CBMI, MM, etc.

ACML, PAKDD

AAMAS, ICRA

ICRA, AAMAS

I used established ML as opposed to contribute to the leading research.

UAI

ICRA, IJCNN, ICANN

I am very new to this area and once attended to ECTI in Thailand.

KDD

ICRA, CDC, IFAC

ICCBR

Question 5

Do you have (or have you had) students in machine learning?

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Yes	29	11.69%	11.79%
No	217	87.5%	88.21%
Sum:	246	100%	100%
Total answered: 246

Question 6

What is your educational background/training/disciplinary background? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Biology	7	1.72%	2.94%
Cognitive science	8	1.96%	3.36%
Computer science	181	44.36%	76.05%
Economics	4	0.98%	1.68%
Electrical engineering	34	8.33%	14.29%
Engineering sciences	22	5.39%	9.24%
Machine Learning	43	10.54%	18.07%
Mathematics	48	11.76%	20.17%
Neuroscience	5	1.23%	2.1%
Operations research	3	0.74%	1.26%
Physics	15	3.68%	6.3%
Social science	1	0.25%	0.42%
Statistics	22	5.39%	9.24%
Other:	15	3.68%	6.3%
Sum:	408	100%	100%
Total answered: 238

Text input

Music

.

biomedical engineering

signal processing

Bioinformatics

Information Technology

Computer Vision

Philosophy

Theory

signal processing

Intelligent Systems

Computer Engineering

Chemistry

Mechanical Engg.

Engineering Cybernetics

Question 7

Did your educational background prepare you well for research in machine learning?

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Poorly prepared)	13	5.24%	5.46%
2	65	26.21%	27.31%
3	115	46.37%	48.32%
4 (Well prepared)	45	18.15%	18.91%
Not answered:	10	4.03%	-
Sum:	248	100%	100%
Total answered: 238

Question 8

What educational backgrounds do your students have? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Cognitive science	1	2.04%	3.7%
Computer science	22	44.9%	81.48%
Economics	2	4.08%	7.41%
Electrical engineering	7	14.29%	25.93%
Engineering sciences	5	10.2%	18.52%
Machine Learning	5	10.2%	18.52%
Mathematics	4	8.16%	14.81%
Physics	1	2.04%	3.7%
Other:	2	4.08%	7.41%
Sum:	49	100%	100%
Total answered: 27

Text input

Computational Linguistics

Mechanical Engineering with major in Control Engineering

Question 9

Did the educational backgrounds of your students prepare them well for studying machine learning?

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Poorly prepared)	5	2.02%	18.52%
2	9	3.63%	33.33%
3	10	4.03%	37.04%
4 (Well prepared)	3	1.21%	11.11%
Not answered:	221	89.11%	-
Sum:	248	100%	100%
Total answered: 27

Question 10

In your opinion, what will constitute good educational backgrounds for a graduate student entering machine learning? (You may select as many as you like).

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Biology	4	0.41%	1.72%
Cognitive science	34	3.46%	14.59%
Computer science	193	19.65%	82.83%
Economics	7	0.71%	3%
Electrical engineering	42	4.28%	18.03%
Engineering sciences	22	2.24%	9.44%
Machine Learning	172	17.52%	73.82%
Mathematics	185	18.84%	79.4%
Neuroscience	19	1.93%	8.15%
Operations research	50	5.09%	21.46%
Physics	49	4.99%	21.03%
Psychology	10	1.02%	4.29%
Social science	8	0.81%	3.43%
Statistics	183	18.64%	78.54%
Other:	4	0.41%	1.72%
Sum:	982	100%	100%
Total answered: 233

Text input

.

Optimization

Text input

applied mathematics

Some basic courses in optimization and Decision Sciences too are significant in Machine Learning

Actually, I think many different backgrounds could be beneficial for the community.
This seems to be the most obvious to me, though.

I'm inclined to select almost all of them. Some experience in Maths is the only common denominator I see in all of them.

It depends on the focus - I answered for more theoretically inclined machine learning but some researchers are interested in biological systems or in the learning done in the human brain, for them other disciplines are relevant.

Encountered many people with very weak math preparation due to coming from undergrad computer science background, which offers exceedingly poor math preparation even at top universities. Often run into (otherwise good and motivated) machine learning graduate students with very poor understanding of linear algebra other than the most basic symbol pushing, dodgy understanding of what random variables are, and essentially no capacity to absorb more sophisticated concepts like RKHS, Bayesian nonparametrics, etc.

Later on, one has to learn about the application, too. But since applications are widespread, one cannot know everything beforehand. My biology knowledge has helped me partially.

Computer science training is essential because MLer need to do coding.

From my perspective, a requrement for success is first of all a pure or "semi-pure" mathematic background (incl basic statstics) combined with a more physical aspect such as physics or EE a good basis. Computer Science is to broard to be mentioned, yet of course important as a pratical tool. But the mathematics seems to be more and more important to understand the very complicated models.

I guess Machine Learning would prepare a student well for Machine Learning. However, since that is non-informative, I chose disciplines OTHER THAN Machine Learning that would give a student a strong enough background to do well in Machine Learning.

A solid mathematical background and the ability to write code are most important in my opinion.

Knowledge of: neural networks, statistics, computational neuroscience, computer programming, mathematical optimization, game theory, information theory, artificial intelligence, computational intelligence, machine learning, complexity theory. Some knowledge of engineering and physics might be advantageous.

Specifically, a solid understanding of both linear algebra and significant programming experience as a hobby, not as a job, are the right combination. It is hard to find both together. Those who lack the math skills will spend all their time catching up, while the types who choose to use high-level programming languages rarely have drive sufficient to undertake ambitious projects.

It's all about math and stats. Neuroscience and biology, etc., offer convenient high-level metaphors for some approaches, but they disappear when you look at the details.

practical experiences, e.g. with one of the many ml competitions cannot be overvalued

The fields I chose will give a good fundamental background in machine learning, though a background in the other fields listed will give imperative domain knowledge and at least one should be studied in some depth.

What I've missed the most is a mathematically rigorous, unified introduction to statistics and the parts of machine learning closest to statistics.

Bayesian Statistics

statistical physics in particular.

Statistics is really important... In Iran it didn't receive much attention in my undergrad school but I studied hard myself.

Question 11

What area(s) in machine learning do you work in? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Bayesian statistics	99	10.43%	46.92%
Control and planning	25	2.63%	11.85%
Deep learning	13	1.37%	6.16%
Dimensionality reduction	53	5.58%	25.12%
Graphical models	91	9.59%	43.13%
Information theory	18	1.9%	8.53%
Kernel methods	46	4.85%	21.8%
Learning theory	21	2.21%	9.95%
Manifold learning	12	1.26%	5.69%
Model selection	32	3.37%	15.17%
Neural networks	31	3.27%	14.69%
Nonparametric methods	46	4.85%	21.8%
Online learning	38	4%	18.01%
Optimization	48	5.06%	22.75%
Reinforcement learning	39	4.11%	18.48%
Relational learning	18	1.9%	8.53%
Semisupervised learning	44	4.64%	20.85%
Sparse learning	36	3.79%	17.06%
Statistical physics of learning	3	0.32%	1.42%
Structured learning	38	4%	18.01%
Supervised learning	85	8.96%	40.28%
Time series modelling	38	4%	18.01%
Unsupervised learning	69	7.27%	32.7%
Other:	6	0.63%	2.84%
Sum:	949	100%	100%
Total answered: 211

Text input

Causal Inference

Ranking

Detectors combining

Transfer Learning

NLP

Question 12

What other area(s) do you work in? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
None	11	1.78%	5.21%
Artificial intelligence	50	8.08%	23.7%
Bioinformatics	25	4.04%	11.85%
Brain, medical imaging	10	1.62%	4.74%
Cognitive science	6	0.97%	2.84%
Collaborative filtering	23	3.72%	10.9%
Communications	4	0.65%	1.9%
Computer vision	48	7.75%	22.75%
Computer science	59	9.53%	27.96%
Databases	8	1.29%	3.79%
Data mining	45	7.27%	21.33%
Electrical engineering	11	1.78%	5.21%
Genetics	13	2.1%	6.16%
Hardware technologies	1	0.16%	0.47%
Multimedia processing	6	0.97%	2.84%
Linguistics	15	2.42%	7.11%
Image processing	27	4.36%	12.8%
Information retrieval	32	5.17%	15.17%
Mathematics	10	1.62%	4.74%
Natural language processing	34	5.49%	16.11%
Network analysis	13	2.1%	6.16%
Neural data analysis	4	0.65%	1.9%
Neuroscience	11	1.78%	5.21%
Operations research	5	0.81%	2.37%
Physics	2	0.32%	0.95%
Psychology	5	0.81%	2.37%
Robotics	22	3.55%	10.43%
Signal processing	24	3.88%	11.37%
Social science	6	0.97%	2.84%
Speech processing	9	1.45%	4.27%
Statistics	23	3.72%	10.9%
Systems biology	8	1.29%	3.79%
Text and web analysis	40	6.46%	18.96%
Other:	9	1.45%	4.27%
Sum:	619	100%	100%
Total answered: 211

Text input

multi-agent planning

Event Recognition

Economics

Computer Vision

Optimal Control

Ubiquitous Computing

Econometrics

Business Research

Question 13

In your opinion, what are core (foundation) areas of machine learning? (You may select as many as you see fit)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Bayesian statistics	141	9.66%	67.14%
Control and planning	22	1.51%	10.48%
Deep learning	20	1.37%	9.52%
Dimensionality reduction	59	4.04%	28.1%
Graphical models	103	7.06%	49.05%
Information theory	88	6.03%	41.9%
Kernel methods	79	5.41%	37.62%
Learning theory	113	7.75%	53.81%
Manifold learning	18	1.23%	8.57%
Model selection	57	3.91%	27.14%
Neural networks	43	2.95%	20.48%
Nonparametric methods	52	3.56%	24.76%
Online learning	44	3.02%	20.95%
Optimization	123	8.43%	58.57%
Reinforcement learning	74	5.07%	35.24%
Relational learning	19	1.3%	9.05%
Semisupervised learning	51	3.5%	24.29%
Sparse learning	29	1.99%	13.81%
Statistical physics of learning	19	1.3%	9.05%
Structured learning	36	2.47%	17.14%
Supervised learning	127	8.7%	60.48%
Time series modelling	24	1.64%	11.43%
Unsupervised learning	115	7.88%	54.76%
Other:	3	0.21%	1.43%
Sum:	1459	100%	100%
Total answered: 210

Text input

Hard to Say

most of the above

not interested in replying

Question 14

In your opinion, machine learning is a subfield of statistics.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	29	11.69%	14.36%
2	45	18.15%	22.28%
3	85	34.27%	42.08%
4 (Agree)	43	17.34%	21.29%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 15

In your opinion, machine learning is a subfield of computer science.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	10	4.03%	4.95%
2	40	16.13%	19.8%
3	89	35.89%	44.06%
4 (Agree)	63	25.4%	31.19%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 16

In your opinion, machine learning is a subfield of cognitive science.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	82	33.06%	40.59%
2	84	33.87%	41.58%
3	28	11.29%	13.86%
4 (Agree)	8	3.23%	3.96%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 17

In your opinion, machine learning is a subfield of neuroscience.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	103	41.53%	50.99%
2	72	29.03%	35.64%
3	20	8.06%	9.9%
4 (Agree)	7	2.82%	3.47%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 18

In your opinion, machine learning is a subfield of engineering.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	57	22.98%	28.22%
2	55	22.18%	27.23%
3	65	26.21%	32.18%
4 (Agree)	25	10.08%	12.38%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 19

In your opinion, machine learning is a subfield of artificial intelligence.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	19	7.66%	9.36%
2	29	11.69%	14.29%
3	77	31.05%	37.93%
4 (Agree)	78	31.45%	38.42%
Not answered:	45	18.15%	-
Sum:	248	100%	100%
Total answered: 203

Question 20

In your opinion, machine learning is an interdisciplinary area.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	2	0.81%	0.99%
2	9	3.63%	4.46%
3	47	18.95%	23.27%
4 (Agree)	144	58.06%	71.29%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 21

In your opinion, machine learning is a a discipline distinct from other disciplines.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	31	12.5%	15.35%
2	66	26.61%	32.67%
3	64	25.81%	31.68%
4 (Agree)	41	16.53%	20.3%
Not answered:	46	18.55%	-
Sum:	248	100%	100%
Total answered: 202

Question 22

Any further comments regarding the relationship of machine learning to other disciplines?

Text input

I see it as a subfield of AI that is an intersection of statistics/information theory with computer science and optimization.

Applied mathematics

machine learning provides useful tools that can be applied to solve problems in many other disciplines.

What i feel or enjoy about Machine Learning is that no one can take ownership to it and there is no boundary. It is much an Electrical Engineer's as well as a Statistician and it is our responsibility to nurture it. We are seeing some interesting work in the current days and i hope to see more and be a part of it.

It is a pity that it is not more promoted, e.g. in Maths Departments.

it is a tool for other disciplines

In our lab, we are specifically digging connections between signal processing and machine learning.
There's a lot of issues both fields try to adress. We use a lot of common tools and methods. Still, we often have pretty different views. That's stimulating.

Machine Learning, in general, is tightly associated with statistics, mathematical logic and computer science. However, since Machine Learning is applied into a variety of different domains (e.g natural language processing, medical etc), it has strong relationship with the application domain.

There seem to be at least two parts of machine learning. Some people just go by standard mathematics, algorithms, statistics, optimization, and so on. Other people attempt, with varying degrees of success, to involve all kinds of "neurological" and "biological" interpretation. It seems a wide range of disciplines can follow the work in the former, but the work in the latter is less respected in other technical fields, and even by the other more technically-oriented part of machine learning.

draws from other disciplines (statistics, optimization, ...)
and yields methods for other fields (applications)

Machine learning is a marriage of artificial intelligence, statistics, and computer science.

Machine learning is its own discipline and it is a subfield of computer science. It has strong connections to other fields, but it is defined by a distinct collection (although items in the collection may not be unique) of attitudes, problems, techniques, tools, and concepts.

Lance fortnow recently lamented the lack of role that theoretical computer science has in modern machine learning. I agree with him that it should play an increased role in both machine learning education AND practice.

wow that was confusing choices :)

Machine Learning is a useful category, I personally tend to exclude Reinforcement Learning from it and place RL as a sibling of ML under AI. Integrated AI systems are not based solely on ML or RL (but at least on both, that's why usually RL viewed as part of ML). ML can be used on its own in engineering or software systems.

An interesting poling question with degree of agree/disagree might be "On average, machine learning in Electrical Engineering is more theoretical and machine learning in computer science is more focused on applications."

ML has become so diverse that even veteran experts often lack a sufficiently deep understanding of the motivations and foundations of the areas where I operate. Hence, greater fragmentation and specialization within the top ML conferences and journals would be very welcome. In the past, a rift had started to form between the neural approaches and the more traditional classification models. The community, however, worked to mend the differences and hold together as a unified field. Now, a new divide has started to form between the statisticians/Bayesian/graphical-model approaches and the rest of the community. Unfortunately, it seems we are again working hard to hold it all together. The science would be better served by promoting modularization, rather than creating a bloated and monolithic field in which communication and point-of-view difficulties drag at advancement.

I guess you miss the empirical process theory Although some may consider it as a subfield of statistics.

I've learned it as part of computer science at two schools, but it fits almost as well under statistics. (AI is also part of CS.)

I'm very fond one analogy on Hal Daume's blog (nlpers.blogspot.com):
Machine Learning is computational statistics that doesn't care about the statistics
Computational statistics is machine learning that doesn't care about the computation

None

From the previous page, the subsetting relation between ML and Stat is much stronger than others.

I think that right now, the bread and butter of ML is finding models that can capture most of the important structure in data, while still being tractactable. So any discipline that is used to approximate computation (like computational chemistry) would feel at home in ML.

In order to create new models, insight into disciplines such as biology and cognitive science may help to model the success of living organisms, however realistically machine learning is more strongly based in statistics and computer science. Though a physics/ applied mathematics background would be useful as these subjects study the creation of new models!

Question 23

In your opinion, in the university, machine learning should ideally be a group within computer science.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	9	3.63%	4.57%
2	34	13.71%	17.26%
3	79	31.85%	40.1%
4 (Agree)	75	30.24%	38.07%
Not answered:	51	20.56%	-
Sum:	248	100%	100%
Total answered: 197

Question 24

In your opinion, in the university, machine learning should ideally be a group within neuroscience.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	99	39.92%	50.25%
2	75	30.24%	38.07%
3	20	8.06%	10.15%
4 (Agree)	3	1.21%	1.52%
Not answered:	51	20.56%	-
Sum:	248	100%	100%
Total answered: 197

Question 25

In your opinion, in the university, machine learning should ideally be a group within engineering.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	50	20.16%	25.25%
2	80	32.26%	40.4%
3	46	18.55%	23.23%
4 (Agree)	22	8.87%	11.11%
Not answered:	50	20.16%	-
Sum:	248	100%	100%
Total answered: 198

Question 26

In your opinion, in the university, machine learning should ideally be a group within statistics.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	22	8.87%	11.11%
2	75	30.24%	37.88%
3	79	31.85%	39.9%
4 (Agree)	22	8.87%	11.11%
Not answered:	50	20.16%	-
Sum:	248	100%	100%
Total answered: 198

Question 27

In your opinion, in the university, machine learning should ideally be a group within psychology.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	133	53.63%	67.17%
2	58	23.39%	29.29%
3	6	2.42%	3.03%
4 (Agree)	1	0.4%	0.51%
Not answered:	50	20.16%	-
Sum:	248	100%	100%
Total answered: 198

Question 28

In your opinion, in the university, machine learning should ideally be a spread across multiple departments.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	39	15.73%	19.8%
2	44	17.74%	22.34%
3	59	23.79%	29.95%
4 (Agree)	55	22.18%	27.92%
Not answered:	51	20.56%	-
Sum:	248	100%	100%
Total answered: 197

Question 29

In your opinion, in the university, machine learning should ideally be an interdisciplinary centre.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	11	4.44%	5.56%
2	17	6.85%	8.59%
3	83	33.47%	41.92%
4 (Agree)	87	35.08%	43.94%
Not answered:	50	20.16%	-
Sum:	248	100%	100%
Total answered: 198

Question 30

In your opinion, in the university, machine learning should ideally be its own department.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	37	14.92%	18.78%
2	55	22.18%	27.92%
3	50	20.16%	25.38%
4 (Agree)	55	22.18%	27.92%
Not answered:	51	20.56%	-
Sum:	248	100%	100%
Total answered: 197

Question 31

In your opinion, in the university, machine learning should be taught at the graduate level.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	12	4.84%	6.15%
2	18	7.26%	9.23%
3	44	17.74%	22.56%
4 (Agree)	121	48.79%	62.05%
Not answered:	53	21.37%	-
Sum:	248	100%	100%
Total answered: 195

Question 32

In your opinion, in the university, machine learning should be taught as a specialist track in a related undergraduate programme.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	12	4.84%	6.09%
2	27	10.89%	13.71%
3	74	29.84%	37.56%
4 (Agree)	84	33.87%	42.64%
Not answered:	51	20.56%	-
Sum:	248	100%	100%
Total answered: 197

Question 33

In your opinion, in the university, machine learning should be taught in a dedicated undergraduate programme.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	33	13.31%	16.92%
2	75	30.24%	38.46%
3	41	16.53%	21.03%
4 (Agree)	46	18.55%	23.59%
Not answered:	53	21.37%	-
Sum:	248	100%	100%
Total answered: 195

Question 34

In your opinion, in which departments should machine learning be taught?

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Biology	31	3.07%	15.74%
Cognitive science	64	6.34%	32.49%
Computer science	186	18.42%	94.42%
Economics	38	3.76%	19.29%
Electrical engineering	61	6.04%	30.96%
Engineering sciences	72	7.13%	36.55%
Machine Learning	143	14.16%	72.59%
Mathematics	83	8.22%	42.13%
Neuroscience	57	5.64%	28.93%
Operations research	56	5.54%	28.43%
Physics	25	2.48%	12.69%
Psychology	23	2.28%	11.68%
Social science	19	1.88%	9.64%
Statistics	148	14.65%	75.13%
Other:	4	0.4%	2.03%
Sum:	1010	100%	100%
Total answered: 197

Text input

All

Informatics

Information Systems

Mechanical Engineering

Question 35

Any further comments regarding the place of machine learning within the university?

Text input

I think this will be the last field to have humans replaced by computers, so having a larger proportion of students go into it since early in their academic career is probably a good idea.

in my opinion, the core of machine learning is mostly closely related to statistics and computer science, and requiring considerable other domain knowledge from both.

I've studied maths as an undergraduate student, not so much after that.
I recently found out machine learning started giving me stronger and deeper insights and understanding on different fields of mathematics (mainly in linear algebra, numerical analysis, statistics) which I had found to be pretty disconnected from any reality.
In fact, I think machine learning could be a very nice way to get into various theoretical of applied fields, in undergraduate programmes.

Doing machine learning properly requires background in a number of different topics, so however and wherever it is taught, the students should be expected to have appropriate background in algorithms, complexity, programming, basic statistics, probability, linear algebra, optimization, some real analysis, and so on. Many courses just don't list many of these things as requirements but then end up relying on all these topics anyway, which leads to trouble for many students.

It should be in the same department where AI is.

I have no experience with any department of "Operations research" so can't tell about its relation to machine learning (although it sounds like a department with many AI applications).

Spread across separate departments is okay. There should just be an unprecedented amount of collaboration between the groups. Some schools do this pretty well (UWisc - Madison for one). Some schools fail spectacularly (UWash for one).

Computer science should be split into "IT", "software development" and "computational science". Then, ML clearly belongs as a part of computational science. If those three fields must be all lumped together under the banner of "computer science", then ML is no longer such a good fit, and probably belongs in the math department.

Question 36

What brought you into machine learning? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
I want to use machine learning techniques to analyse data.	128	27.71%	66.32%
I want to understand how the brain works.	43	9.31%	22.28%
I want to understand the fundamental principles of learning and intelligence.	95	20.56%	49.22%
I want to understand existing machine learning techniques and develop new ones.	111	24.03%	57.51%
I want to build intelligent computers or robots.	85	18.4%	44.04%
Sum:	462	100%	100%
Total answered: 193

Text input

To be more specific, I am more interested in *replicating* what the brain can do, whether by its algorithms or novel ones.

Actually, I was working on classification of musical signals, not being really aware that machine learning was a "field on its own". I found a related PhD by chance and ent for it.

It was just a tool for a certain application I was interested in (computer vision). I'd already been using machine learning for a long time before becoming interested in studying it.

I want to be able to make accurate predictions.

I participated in a competition as part of a lab course and that got me hooked.

I want to know one thing which is broadly applicable. That thing is machine learning.

I want to build cognitive tutors.

I want to push robots and autonomous sytems to their limits where first principle models are not available anymore or are too complex to derive. I want to use methods from machine learning to close this gap.

Question 37

In your opinion, what are the goals of machine learning? (You may select more than one)

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
To understand the fundamental principles of learning and intelligence.	114	20.18%	58.76%
To analyze and extract useful information from data.	152	26.9%	78.35%
To understand how the brain works.	38	6.73%	19.59%
To build smarter computers or robots.	110	19.47%	56.7%
To develop algorithms for making accurate predictions based on data.	151	26.73%	77.84%
Sum:	565	100%	100%
Total answered: 194