Faculty

Report info

Report date	Tuesday, May 17, 2011 2:49:59 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	188

Number of completed responses	162

Question 1

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

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Yes	184	97.87%	97.87%
No	4	2.13%	2.13%
Sum:	188	100%	100%
Total answered: 188

Question 2

What do you do?

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Faculty at an academic institution	188	100%	100%
Sum:	188	100%	100%
Total answered: 188

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
Cognitive science	5	2.01%	2.67%
Computer science	145	58.23%	77.54%
Electrical engineering	15	6.02%	8.02%
Engineering sciences	8	3.21%	4.28%
Machine Learning	23	9.24%	12.3%
Mathematics	11	4.42%	5.88%
Neuroscience	3	1.2%	1.6%
Operations research	2	0.8%	1.07%
Physics	2	0.8%	1.07%
Psychology	2	0.8%	1.07%
Social science	1	0.4%	0.53%
Statistics	23	9.24%	12.3%
Other:	9	3.61%	4.81%
Sum:	249	100%	100%
Total answered: 187

Text input

Computational Linguistics

Informatics and Mathematical Modelling

theoretical computer science

Management

Robotics

English

Informatics

Medicine

Computational Linguistics

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	28	6.01%	18.54%
ACL	20	4.29%	13.25%
AISTATS	26	5.58%	17.22%
ALT	8	1.72%	5.3%
CogSci	6	1.29%	3.97%
COLT	25	5.36%	16.56%
CoNLL	11	2.36%	7.28%
Cosyne	3	0.64%	1.99%
CVPR	11	2.36%	7.28%
ECCV	2	0.43%	1.32%
ECML	37	7.94%	24.5%
EMNLP	13	2.79%	8.61%
ICCV	4	0.86%	2.65%
ICML	77	16.52%	50.99%
IJCAI	33	7.08%	21.85%
IROS	7	1.5%	4.64%
ISMB	6	1.29%	3.97%
NAACL	7	1.5%	4.64%
NIPS	88	18.88%	58.28%
RECOMB	4	0.86%	2.65%
SIGIR	10	2.15%	6.62%
SIGGRAPH	1	0.21%	0.66%
Snowbird	8	1.72%	5.3%
UAI	31	6.65%	20.53%
Sum:	466	100%	100%
Total answered: 151

Text input

ISBA

ISIT,

ICRA

ICPR

ILP

ICPR

ACML

IEEE MLSP

ICDM, ICMLA

ICDM
EGC

IFCS, ERCIM

ISIT

IJCNN,

MLSP, ICASSP

MLSP (Machine Learning for Signal Processing)

KDD

ICRA

ISMB

AAMAS

Statistical conference

RecSys

ICGI (International Conference in Grammatical Inference)

SMBE (Society for Molecular biology and Evolution)

ICGI

ICASSP

ICPR, ICMI, ICDAR, Interspeech, ICASSP

ICMEN

IJCAI
ICONIP
ESANN

ECAI

URSW
Brazilian conferences

ICDM, PKDD, Web Intelligence

IEEE InfoVis, IEEE Visual Analytics Science and Technology, IEEE/Eurographics EuroVis, ACM KDD

ICDM

LPNMR

IDA

ILP, ICLP

SPIE

ICRA ICCBR

ADHO (joint conference of the Association of Literary and Linguistic Computing and the Association of Computing in the Humanities)

IJCNN, ESANN,

PGM, ECSQARU, IDA

ESANN

AAMAS

NAFIPS
IFSA
NASTEC

ICIC, ISDA

SIGKDD

GECCO

VSS

Artificial Intelligence in Medicine

ICANN ESANN ECAI

ICPR
S+SSPR

Icdm kdd

ECAI, ICFCA, IAT

WCCI

KDD

Question 5

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

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Yes	162	86.17%	86.63%
No	25	13.3%	13.37%
Sum:	187	100%	100%
Total answered: 187

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	4	1.19%	2.2%
Cognitive science	9	2.67%	4.95%
Computer science	124	36.8%	68.13%
Electrical engineering	34	10.09%	18.68%
Engineering sciences	12	3.56%	6.59%
Machine Learning	38	11.28%	20.88%
Mathematics	55	16.32%	30.22%
Neuroscience	4	1.19%	2.2%
Operations research	2	0.59%	1.1%
Physics	22	6.53%	12.09%
Psychology	4	1.19%	2.2%
Social science	1	0.3%	0.55%
Statistics	20	5.93%	10.99%
Other:	8	2.37%	4.4%
Sum:	337	100%	100%
Total answered: 182

Text input

Philosophy

robotics

Literature/Linguistics

robotics

Artificial Intelligence

Geography

Linguistics

Philosophy

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	6.91%	7.14%
2	33	17.55%	18.13%
3	68	36.17%	37.36%
4 (Well prepared)	68	36.17%	37.36%
Not answered:	6	3.19%	-
Sum:	188	100%	100%
Total answered: 182

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
Biology	9	2.55%	5.77%
Cognitive science	7	1.98%	4.49%
Computer science	132	37.39%	84.62%
Electrical engineering	39	11.05%	25%
Engineering sciences	29	8.22%	18.59%
Machine Learning	23	6.52%	14.74%
Mathematics	53	15.01%	33.97%
Neuroscience	5	1.42%	3.21%
Operations research	3	0.85%	1.92%
Physics	14	3.97%	8.97%
Psychology	4	1.13%	2.56%
Social science	1	0.28%	0.64%
Statistics	30	8.5%	19.23%
Other:	4	1.13%	2.56%
Sum:	353	100%	100%
Total answered: 156

Text input

Linguistics

Linguistics

Medical Informatics

Linguistics

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)	11	5.85%	7.05%
2	51	27.13%	32.69%
3	72	38.3%	46.15%
4 (Well prepared)	22	11.7%	14.1%
Not answered:	32	17.02%	-
Sum:	188	100%	100%
Total answered: 156

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	5	0.65%	2.87%
Cognitive science	28	3.65%	16.09%
Computer science	148	19.3%	85.06%
Economics	6	0.78%	3.45%
Electrical engineering	38	4.95%	21.84%
Engineering sciences	22	2.87%	12.64%
Machine Learning	132	17.21%	75.86%
Mathematics	138	17.99%	79.31%
Neuroscience	10	1.3%	5.75%
Operations research	41	5.35%	23.56%
Physics	44	5.74%	25.29%
Psychology	6	0.78%	3.45%
Social science	4	0.52%	2.3%
Statistics	141	18.38%	81.03%
Other:	4	0.52%	2.3%
Sum:	767	100%	100%
Total answered: 174

Text input

ognitive modelling

Logic

Fuzzy Logic

AI

Text input

This is hard to answer with a binary tick mark because "goodness" is continuous and qualitative. If I had to order the goodness of background then I would say machine learning > statistics > computer science > physics > maths > electrical engineering.

basic knowledge of probability theory, statistics, algorithmics and linear algebra is extremely important

In my opinion, as machine learning involves a lot of work with maths and a lot of work computers, general maths and general computer science should be seen as necessary fundamentals. The general maths could also be obtained in different fields, e.g. physics. Statistics, especially statistics with a extensive use of probability theory (which I take to be synonymous with Bayesian statistics) is the most valuable more specialized discipline.

Continuous mathematics taught in EE / Physics is much more important than Computer Science.

The most difficult things to teach are: mathematical fluency, hacking. Students can pick these up in any of the above disciplines, but not all do.

CS students lack math and stat; "machine learning" students lack CS and logic and the ability to model.

it is clear from your questions that you want the data to show that CS (as traditionally taught) is a poor preparation for machine learning research, which requires much more math than most CS ugrads ever see... I agree with this point of view. However, one could also argue that stats as an ugrad subject does not produce students with enough curiosity or computer skills

Maths is crucial

CS would be nice, but as it is taught right now, most student lack all of continuous math

no.

"Mathematics" and "Physics" are big domains! How about "scientific computing", "optimization"?
I don't see what social science has to do with ML. By far the most useful background for ML
is Physics, unfortunately I had little luck so far to recruit such students. The great perspective of ML is too little visible for physics students.

Strong abilities in theoretical fields : almost all effective and usefull advances in ML come from fine mathematical properties cleverly used.

Applied mathematics; data-driven thinking

What you need is strong math & coding skills

Difficult to offer the perfect background at the master level. Good graduate courses and summer schools are important at the beginning of a PhD. Further education in additional domains (e.g. biology) are also useful for related applications (e.g. biomedical problems)

To be useful to me students need strong enough maths (calculus, linear alg, porbability, stats)
and CS (programming, but also algorithms ideas) to do the technical work. If they have that, background in e.g. cog sci can be helpful as a source of ideas/motivations.

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	58	6.76%	34.52%
Control and planning	24	2.8%	14.29%
Deep learning	8	0.93%	4.76%
Dimensionality reduction	50	5.83%	29.76%
Graphical models	57	6.64%	33.93%
Information theory	23	2.68%	13.69%
Kernel methods	45	5.24%	26.79%
Learning theory	41	4.78%	24.4%
Manifold learning	15	1.75%	8.93%
Model selection	38	4.43%	22.62%
Neural networks	36	4.2%	21.43%
Nonparametric methods	31	3.61%	18.45%
Online learning	34	3.96%	20.24%
Optimization	31	3.61%	18.45%
Reinforcement learning	42	4.9%	25%
Relational learning	27	3.15%	16.07%
Semisupervised learning	36	4.2%	21.43%
Sparse learning	23	2.68%	13.69%
Statistical physics of learning	4	0.47%	2.38%
Structured learning	33	3.85%	19.64%
Supervised learning	83	9.67%	49.4%
Time series modelling	37	4.31%	22.02%
Unsupervised learning	74	8.62%	44.05%
Other:	8	0.93%	4.76%
Sum:	858	100%	100%
Total answered: 168

Text input

Query Learning

grammatical Inference

numerical integration

soft computing

Regression analysis

Multiple-instance learning

Pattern mining

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	2	0.29%	1.19%
Artificial intelligence	64	9.17%	38.1%
Bioinformatics	32	4.58%	19.05%
Brain, medical imaging	13	1.86%	7.74%
Cognitive science	18	2.58%	10.71%
Collaborative filtering	13	1.86%	7.74%
Communications	4	0.57%	2.38%
Computer vision	37	5.3%	22.02%
Computer science	59	8.45%	35.12%
Databases	5	0.72%	2.98%
Data mining	57	8.17%	33.93%
Electrical engineering	11	1.58%	6.55%
Genetics	10	1.43%	5.95%
Hardware technologies	2	0.29%	1.19%
Multimedia processing	5	0.72%	2.98%
Linguistics	15	2.15%	8.93%
Image processing	25	3.58%	14.88%
Information retrieval	25	3.58%	14.88%
Mathematics	14	2.01%	8.33%
Natural language processing	44	6.3%	26.19%
Network analysis	11	1.58%	6.55%
Neural data analysis	9	1.29%	5.36%
Neuroprosthetics	2	0.29%	1.19%
Neuroscience	13	1.86%	7.74%
Operations research	10	1.43%	5.95%
Physics	3	0.43%	1.79%
Psychology	12	1.72%	7.14%
Robotics	21	3.01%	12.5%
Signal processing	33	4.73%	19.64%
Social science	10	1.43%	5.95%
Speech processing	19	2.72%	11.31%
Statistics	46	6.59%	27.38%
Systems biology	12	1.72%	7.14%
Text and web analysis	34	4.87%	20.24%
Other:	8	1.15%	4.76%
Sum:	698	100%	100%
Total answered: 168

Text input

Pattern recognition

Ecology and Ecosystem management

knowledge discovery in databases

soft computing

Medical Informatics

Video games

Atmospheric and Hydrological Sciences

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	101	8.98%	60.12%
Control and planning	25	2.22%	14.88%
Deep learning	11	0.98%	6.55%
Dimensionality reduction	46	4.09%	27.38%
Graphical models	64	5.69%	38.1%
Information theory	64	5.69%	38.1%
Kernel methods	56	4.98%	33.33%
Learning theory	108	9.6%	64.29%
Manifold learning	18	1.6%	10.71%
Model selection	51	4.53%	30.36%
Neural networks	36	3.2%	21.43%
Nonparametric methods	37	3.29%	22.02%
Online learning	36	3.2%	21.43%
Optimization	74	6.58%	44.05%
Reinforcement learning	60	5.33%	35.71%
Relational learning	21	1.87%	12.5%
Semisupervised learning	40	3.56%	23.81%
Sparse learning	20	1.78%	11.9%
Statistical physics of learning	11	0.98%	6.55%
Structured learning	25	2.22%	14.88%
Supervised learning	99	8.8%	58.93%
Time series modelling	18	1.6%	10.71%
Unsupervised learning	90	8%	53.57%
Other:	14	1.24%	8.33%
Sum:	1125	100%	100%
Total answered: 168

Text input

Statistics

Probability Theory, Theory of frequentist statistics

these are too specific

Pattern Recognition

statistics (may be non-Bayesian)

Numerical methods, probability

soft computing

State-space search

statistics (not necessarily Bayes), mathematics, geometry, decision theory

stats, maths especially optimization, CS

Empirical process theory

All

Question 14

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

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	18	9.57%	10.71%
2	49	26.06%	29.17%
3	71	37.77%	42.26%
4 (Agree)	30	15.96%	17.86%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

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)	17	9.04%	10.12%
2	25	13.3%	14.88%
3	68	36.17%	40.48%
4 (Agree)	58	30.85%	34.52%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

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)	77	40.96%	45.83%
2	58	30.85%	34.52%
3	24	12.77%	14.29%
4 (Agree)	9	4.79%	5.36%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

Question 17

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

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	110	58.51%	65.48%
2	39	20.74%	23.21%
3	18	9.57%	10.71%
4 (Agree)	1	0.53%	0.6%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

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	30.32%	33.93%
2	54	28.72%	32.14%
3	41	21.81%	24.4%
4 (Agree)	16	8.51%	9.52%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

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	10.11%	11.31%
2	32	17.02%	19.05%
3	65	34.57%	38.69%
4 (Agree)	52	27.66%	30.95%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

Question 20

In your opinion, machine learning is an interdisciplinary area.

Frequency table
Levels	Absolute frequency	Relative frequency	Adjusted relative frequency
1 (Disagree)	1	0.53%	0.6%
2	4	2.13%	2.38%
3	37	19.68%	22.02%
4 (Agree)	126	67.02%	75%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

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)	42	22.34%	25%
2	47	25%	27.98%
3	50	26.6%	29.76%
4 (Agree)	29	15.43%	17.26%
Not answered:	20	10.64%	-
Sum:	188	100%	100%
Total answered: 168

Question 22

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

Text input

I think it is an interdisciplinary area.

Actually in most cases the inclusion between two discipline is too strong an assumption in the previous page, most often there are overlapping and non-overlapping areas

Machine learning investigates the mathematical foundations of epistemology, i.e., inference and statistical modeling in general.

Machine learning is becoming increasingly close to statistics

It is a tool for some disciplines (neuroscience, biology, etc.)
It shares foundations with other disciplines (statistics, optimization, computer science)

Machine learning is a discipline that has striking 'local' theoretical and practical successes, but which has not yet succeeded in the natural core aim of constructing an intelligence through learning.

I don't find the question that relevant (for me personally) to which discipline to assign M.L. (or interesting areas of work in general).

I see ML as an application of statistics to certain kinds of learning and inference problems, such problems arise frequently in engineering, AI, NLP, cognitive science, comp neuroscence, etc.

Very interesting survey; look forward to the results.

It is important to distinguish the problem of machine learning (a subproblem of the more general problem of achieving artificial intelligence in a machine) from specific methods. All too often, ML gets bogged down on details - be it of Bayesian statistics or inductive logic, without paying much attention to what that issue has to do with the grander challenge of autonomous, incremental, constructive learning in the sense of AI.
The question of relation to other areas should then be thought of in this light. Some breadth of coverage, not just of tools but also of problems that are worth solving (ranging from economics to neuroscience) is a must.

Strong relationships to information theory, statistics, game theory, optimization

My view is that ML mainly falls into Computer Science, Applied Mathematics, Statistics, and Operations Research. The distinction with statistics is blurry, but I think they are different mainly in terms of the way problems are approached and the specific theoretical interest (e.g., finite sample bounds vs. consistency).

I generally apply machine learning to Computational Linguistics. I am my elective course, students from varied areas come to learn the concepts. They are from Electrical Engg., Computer Science, Computational Mathematics, Physics.

Since, In India, we do not have so much exposure of Computing to Social Sciences. So, people from various social science do not participate, but every now and then, they do participate in workshops on time series modeling etc.

Aspirationally I would like to see it become more of a mature engineering discipline. It most certainly is not yet

Mathematics

I asnwered that machine learning is not a subfield of statistics, but I feel like what is called "Machine Learning" in this survey is a subfield of statistics...

Machine learning can be useful for web semantics, games theory

The Turing, Rumelhart and Herzberg award all went to machine learning people this year: Valiant, Hinton and Pearl. So machine learning stands on its own. However, almost everyone it is part of the CS or engineering departments. Many statisticians see it as a threat. It is. It's a combination of stats, computing, and optimization and hence a new form of stats that can handle the problems of today. Yet, stats remains more cautious and with better analytical analysis (over-generalizing here). One key difference between stats and ML is the publishing model: articles vs conferences. Optimization folks used to have a lot less respect for ML, but that is changing over the last few years too. Again, the most distinguishing thing about ML is publishing impatience: conferences vs the old journal model. Engineers, still have their own cliques (mostly IEEE). They also have this parallel ML universe: fuzzy-neuro-evolutionary conferences.

Soft Computing

Also a subfield of mathematics and optimization

the best machine learning work tends to carry certain levels of elegance (ideas, algorithms, etc).. This is somewhat difficult to characterize.

My focus is on practical applications of machine learning, so I am not overly concerned whether an algorithm has a basis in neuroscience or not, as long as it works.

ML is not strictly speaking a subfield of any field but it grasps key aspects from CS (AI, algorithmics), Math (optimization at least) and stats and is linked to a very wide variety of other domains where ML is applied (medecine, biology, economics, and many more).

The pilars of machine learning are: statistics, statistical physics, computer science, mathematics , biology and cognitive science

Your questions are quite biased, e.g., not including evolutionary forms of learning.

The questions above are a bit hard to answer nicely without some explanations! Some
answers come from inclusion: if ML is a subfield of AI, and AI is a subfiled of CS, then
MLis a subfield of CS. Re interdisciplinary, it seems to me that ML takes from stats, but also from the algorithmic aspects of e.g. graphical models and optimization from CS.

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)	13	6.91%	8.07%
2	27	14.36%	16.77%
3	69	36.7%	42.86%
4 (Agree)	52	27.66%	32.3%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	111	59.04%	69.81%
2	37	19.68%	23.27%
3	10	5.32%	6.29%
4 (Agree)	1	0.53%	0.63%
Not answered:	29	15.43%	-
Sum:	188	100%	100%
Total answered: 159

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)	49	26.06%	30.43%
2	58	30.85%	36.02%
3	42	22.34%	26.09%
4 (Agree)	12	6.38%	7.45%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	32	17.02%	19.75%
2	56	29.79%	34.57%
3	61	32.45%	37.65%
4 (Agree)	13	6.91%	8.02%
Not answered:	26	13.83%	-
Sum:	188	100%	100%
Total answered: 162

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)	120	63.83%	74.53%
2	31	16.49%	19.25%
3	8	4.26%	4.97%
4 (Agree)	2	1.06%	1.24%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	28	14.89%	17.5%
2	36	19.15%	22.5%
3	56	29.79%	35%
4 (Agree)	40	21.28%	25%
Not answered:	28	14.89%	-
Sum:	188	100%	100%
Total answered: 160

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)	7	3.72%	4.35%
2	27	14.36%	16.77%
3	59	31.38%	36.65%
4 (Agree)	68	36.17%	42.24%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	54	28.72%	33.54%
2	48	25.53%	29.81%
3	33	17.55%	20.5%
4 (Agree)	26	13.83%	16.15%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	1	0.53%	0.62%
2	2	1.06%	1.24%
3	45	23.94%	27.95%
4 (Agree)	113	60.11%	70.19%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	6	3.19%	3.73%
2	32	17.02%	19.88%
3	63	33.51%	39.13%
4 (Agree)	60	31.91%	37.27%
Not answered:	27	14.36%	-
Sum:	188	100%	100%
Total answered: 161

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)	46	24.47%	28.75%
2	58	30.85%	36.25%
3	41	21.81%	25.62%
4 (Agree)	15	7.98%	9.38%
Not answered:	28	14.89%	-
Sum:	188	100%	100%
Total answered: 160

Question 34

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

Frequency table
Choices	Absolute frequency	Relative frequency	Adjusted relative frequency
Biology	32	3.82%	19.39%
Cognitive science	58	6.92%	35.15%
Computer science	159	18.97%	96.36%
Economics	34	4.06%	20.61%
Electrical engineering	52	6.21%	31.52%
Engineering sciences	61	7.28%	36.97%
Machine Learning	112	13.37%	67.88%
Mathematics	49	5.85%	29.7%
Neuroscience	43	5.13%	26.06%
Operations research	41	4.89%	24.85%
Physics	23	2.74%	13.94%
Psychology	26	3.1%	15.76%
Social science	23	2.74%	13.94%
Statistics	118	14.08%	71.52%
Other:	7	0.84%	4.24%
Sum:	838	100%	100%
Total answered: 165

Text input

Linguistics

robotics

geophysics, earth and ocean sciences, environment

Soft Computing

Artificial intelligence

(Computational) Linguistics

Question 35

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

Text input

I wasn't quite sure how to answer these questions. ML should be *taken* by students from various departments, but it should be taught by experts in ML who come from (eg) statistics.

When deciding where to do machine learning, local culture and expertise matters more than the name of department.

I don't believe that ML should be a *track* but do believe strongly that there should be undergraduate course(s) in machine learning.

Machine Learning should be integrated in the undergraduate curriculum in connection with the statistics education.

It shares foundations with CS, applied math, OR, and statistics, so it should be taught in one of those departments (at least at the graduate level).

Ideally CS, Stat, and (to some extent) EE should collaborate on teaching a broad, deep set of courses at the graduate level,
and perhaps just one course at the undergraduate level

34: Haha, I'd not enroll in a machine learning department which does not teach machine learning!

large panel of applications (so not linked to a specific domain) ; its mathematical background should be known by every graduate student in science (where mathematics are a usual tool)

Many of the other departments could benefit from some machine learning, but primary practioners should probably come from stats, CS or engineering.

Soft Computing includes Machine Learning. Soft Computing is a generic term.

Q 31 is a bit unclear -- does it mean *only* at grad level?

I don't think it really makes sense to have an u/grad degree in ML. It is fine as a specialization
in e.g. CS.

Having dealt with interdisciplinaryity at our univeristy, I think it is fine to teach ML in CS, but there is a need to open this up to students from other depts.

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.	91	24.86%	58.33%
I want to understand how the brain works.	31	8.47%	19.87%
I want to understand the fundamental principles of learning and intelligence.	94	25.68%	60.26%
I want to understand existing machine learning techniques and develop new ones.	86	23.5%	55.13%
I want to build intelligent computers or robots.	64	17.49%	41.03%
Sum:	366	100%	100%
Total answered: 156

Text input

There was a post there

I didn't intentionally go into M.L.; I work on some stuff (cluster analysis, model selection etc.) that at some point people started to assign to M.L., so it basically sucked me in.

Sheer luck

my research (in statistics and computer science) often involves problems and methods that intersect the area of machine learning research.

I am a researcher working the area of computational linguistics, were in order to train our models we require lost of data which trained using different supervised/semi-supervised approaches.

but we should be modest (e.g. the word "intelligent" is too strong

another connected field is assisted-computer instruction

Decision making from uncertain data very rapidly becomes a core asset in about any of the natural
sciences and serious applications (e.g., medical). ML does not right now live up to this new role,
focussing on overly fancy models instead, and that's why other fields (signal processing, ...) do
the job. I'd want to help changing that.

strong links between ML and core concepts in physics (invariant search)

I want to understand life. Learning was the key element that throughout evolution shaped life into its diverse forms today.

I want to understand myself :)

I want to solve cool (but very difficult) problems.

More or less by accident. (My advisor was starting a project in machine learning when I was starting my thesis.)

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.	94	20.17%	59.12%
To analyze and extract useful information from data.	128	27.47%	80.5%
To understand how the brain works.	32	6.87%	20.13%
To build smarter computers or robots.	83	17.81%	52.2%
To develop algorithms for making accurate predictions based on data.	129	27.68%	81.13%
Sum:	466	100%	100%
Total answered: 159