Macadamia: Master's Programme in Machine Learning and Data Mining
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Macadamia: Master's Programme in Machine Learning and Data Mining Presentation Transcript

  • 1. Macadamia: Master’s Programme in Machine Learning and Data Mining May 6, 2008 Tapani Raiko, Kai Puolamäki, Juha Karhunen, Jaakko Hollmén, Antti Honkela, Samuel Kaski, Heikki Mannila, Erkki Oja, and Olli Simula Teaching Machine Learning: Workshop on open problems and new directions. Saint-Étienne, France
  • 2. Macadamia = Machine learning and Data mining Macadamia is a Master's programme in Machine learning and Data mining at Helsinki University of Technology, Finland. The programme is given by the Department of Information and Computer Science known for its pioneering research and education in this field. The Master of Science degree obtained in this programme during a span of two years enables the graduates to enter the IT industry in Finland or world-wide. The degree also has seamless continuation to doctoral studies for those interested in deeper research and development in machine learning and data mining.
  • 3. Machine learning and Data mining • Machine learning researchers often use probabilistic methods • Data mining research algorithmic (and in combination with probabilistic methods!) • Active research in both topics • Interaction useful: interesting things happen at the intersection
  • 4. Department of Information and Computer Science T3060, http://www.ics.tkk.fi/ Finnish: Tietojenkäsittelytieteen laitos Constituent laboratories (pre-2008): Laboratory of Computer and Information Science Laboratory for Theoretical Computer Science AB HELSINKI UNIVERSITY OF TECHNOLOGY Department of Information and Computer Science – 1/6
  • 5. Resources (averages 2004–06) Professors: 9 Other personnel: 108 py/yr budget funding 52 py/yr external funding 56 py/yr Expenditures: 5.1 Me/yr budget funding 2.7 Me/yr (incl. overhead transfers from external funding) external funding 2.4 Me/yr AB HELSINKI UNIVERSITY OF TECHNOLOGY Department of Information and Computer Science – 2/6
  • 6. Degrees and teaching Numbers (averages 2004–06) M.Sc. (Tech.): 29/yr Dr.Sc. (Tech.): 9/yr ocr: 6800/yr Four majors: computer and information science, theoretical computer science, computational and cognitive biosciences, language technology Three international Master’s Programmes: Bioinformatics (MBI), Foundations of Advanced Computing (FAdCo), Machine Learning and Data Mining (Macadamia) Graduate schools (positions in 2006) Helsinki GS in Computer Science and Engineering (8) GS in Comput. Biology, Bioinformatics, and Biometry (1) AB GS of Language Technology in Finland (1) HELSINKI UNIVERSITY OF TECHNOLOGY GS in Comput. Methods of Information Technology (3) and Computer Science – 3/6 Department of Information
  • 7. Research areas Algorithms and methods for adaptive informatics Multimodal interfaces Bioinformatics and neuroinformatics Computational cognitive systems Adaptive informatics applications Computational logic Combinatorial algorithms and computational complexity Cryptographic techniques and secure protocols Computer-aided software quality control (verification) AB HELSINKI UNIVERSITY OF TECHNOLOGY Department of Information and Computer Science – 4/6
  • 8. People Teaching and supervision for Macadamia students is given by an enthusiastic and experienced group headed by world leaders in this research field. They belong to two national Centres of Excellence, the Adaptive Informatics Research Centre and the From Data to Knowledge Research Centre. The host laboratory is a partner in several Finnish graduate schools. The professors responsible for Macadamia are:
  • 9. s given in the programme. The size of the courses are given in credit points (ECTS). mme is 120 ECTS. Note that the Special Courses have a varying topic (5–6 topics per cluded in the curriculum. Obligatory courses ECTS IT-Services at TKK 2 English language tests / course 3 Machine Learning: Basic Principles 5 Machine Learning and Neural Networks 5 Machine Learning: Advanced Probabilistic Methods 5 Algorithmic methods of data mining 5 Information Visualization 5 Research Project in Computer and Information Science 5–10 Master’s thesis 30 Relevant courses ECTS Computer Vision 5 Statistical Natural Language Processing 5 High-Throughput Bioinformatics 5 Signal Processing in Neuroinformatics 5
  • 10. Machine Learning: Advanced Probabilistic Methods 5 Algorithmic methods of data mining 5 Information Visualization 5 Research Project in Computer and Information Science 5–10 Master’s thesis 30 Relevant courses ECTS Computer Vision 5 Statistical Natural Language Processing 5 High-Throughput Bioinformatics 5 Signal Processing in Neuroinformatics 5 Image Analysis in Neuroinformatics 5 Special Course in Computer and Information Science I–VI 3–7 Introduction to Bayesian Modelling 5 Combinatorial Models and Stochastic Algorithms 6 Search problems and algorithms 4 Parallel and distributed systems 4 Cryptography and data security 4 Computational Complexity Theory 5 Finnish 1A 2 Finnish 1B 2 Finnish 2A 2 Finnish 2B 2 Topics of Special Courses during 2006–2008 ECTS
  • 11. Parallel and distributed systems 4 Cryptography and data security 4 Computational Complexity Theory 5 Finnish 1A 2 Finnish 1B 2 Finnish 2A 2 Finnish 2B 2 Topics of Special Courses during 2006–2008 ECTS Gaussian Processes for Machine Learning 6 Popular Algorithms in Data Mining and Machine Learning 5 Reinforcement Learning — Theory and Applications 6 Multimedia Retrieval 5 Introductory Elements of Functional Data Analysis 7 Independent Component Analysis 6 Information Networks 6 Variable Selection for Regression 6 Nonlinear Dimensionality Reduction 6 Modeling and Simulating Social Web 4 Decision support with data analysis 5 Data analysis and environmental informatics 5
  • 12. T-61.3030 Principles of Neural Computing T-61.3050 Machine Learning: Basic Principles T-61.5030 Advanced Course in Neural Computing T-61.5130 Machine Learning and Neural Networks T-61.5040 Learning Models and Methods T-61.5140 Machine Learning: Advanced Probabilistic Methods Table: Correspondences in degree requirements. Machine Learning Course Reform Old course (before Autumn 2007) New course T-61.3050 Machine Learning: Basic Principles T-61.5040 Learning Models and Methods T-61.5140 Machine Learning: Advanced Probabilistic Methods T-61.3030 Principles of Neural Computing T-61.5130 Machine Learning and Neural Networks T-61.5030 Advanced Course in Neural Computing • Three courses were completely reformed Table: Approximate topical correspondeces. last autumn: increasing the weight of machine AB See http://www.cis.hut.fi/Opinnot/T-61.3050/oldcourses learning at the cost ofT-61.3050 computing Kai Puolam¨ki a neural • All of these courses are lectured every year
  • 13. Course Bureaucracy Chapter 1: Introduction T-61.3050 Machine Learning: Basic Principles Introduction Kai Puolam¨ki a Laboratory of Computer and Information Science (CIS) Department of Computer Science and Engineering Helsinki University of Technology (TKK) Autumn 2007 AB Kai Puolam¨ki a T-61.3050
  • 14. General Information Course Bureaucracy Relation to Old Courses Chapter 1: Introduction Contents of the Course How to Pass the Course You will get 5 cr for passing this course. Requirements for passing the course: Pass the exercise work. The exercise work should be submitted by 2 January 2008. More instructions will appear in a few weeks time. Pass the examination. You can participate to the examination after passing the exercise work (exception: you can participate to the December examination before passing the exercise work; you’ll then pass the course if you pass the exercise work). Optional, but useful: Lectures. Problem sessions. Reading the book and other material. AB Kai Puolam¨ki a T-61.3050
  • 15. General Information Course Bureaucracy Relation to Old Courses Chapter 1: Introduction Contents of the Course Literature The course follows a subset of the book: Alpaydin, 2004. Introduction to Machine Learning. The MIT Press. Additionally, there will also be a PDF chapter on algorithmics (complexity of problems, local minima etc.) to be distributed from the course web site. The lecture slides are available for download from the course web site. I have also given Edita a permission to print them on request. You might also find the material — especially the errata and slides — at the Alpaydin’s web site (see the link at the course web site) useful. AB Kai Puolam¨ki a T-61.3050
  • 16. General Information Course Bureaucracy Relation to Old Courses Chapter 1: Introduction Contents of the Course Very Preliminary Plan of the Topics Supervised learning, Bayesian decision theory, probability distributions and parametric methods, multivariate methods, clustering (mostly Alpaydin’s chapters 1–7 and appendix A) Algorithmic issues in machine learning, such as hardness of problems, approximation techniques and their features (such as local minima), time and memory complexity in data analysis (separate PDF chapter to be distributed from the course web site) Nonparametric methods (Alpaydin 8.1–8.2), linear discrimination (Alpaydin 10.1–10.8), assessing and comparing classification algorithms (Alpaydin’s chapter 14) I’ll try to keep the Alpaydin’s ordering of topics, and emphasize principles rather than to go through all possible algorithms and methods. AB Kai Puolam¨ki a T-61.3050
  • 17. Prof. J. Karhunen T-61.5130 Machine Learning and Neural Networks T-61.5130 Machine Learning and Neural Networks (5 cr) General information on the course Autumn 2007 Prof. Juha Karhunen http://www.cis.hut.fi/Opinnot/T-61.5130/ Helsinki University of Technology, Espoo, Finland 1
  • 18. Course materials • All the course materials will be in English. • There is no satisfactory single book suitable for this course. • However, a large portion of the course is based on the book: • F. Ham and I. Kostanic, Principles of Neurocomputing for Science and Engineering, McGraw-Hill 2001. • This book will be complemented by some material from the book S. rof. J. Karhunen “Neural Networks: A Comprehensive Foundation”, 2nd Neural Ne Haykin, T-61.5130 Machine Learning and ed., Prentice-Hall, 1998. • That previously used book is too extensive for our course. Helsinki University of Technology, Espoo, Finland • Furthermore, independent component analysis is covered from a separate review article. • Ham’s and Kostanic’s book is quite expensive (some 160 USD). • And we shall cover only parts of the Chapters 1-5 from it.
  • 19. lecture(s), too. Prof. J. Karhunen T-61.5130 Machine Learning and Neural Networks Planned contents of the course • Introduction to neural networks. e following Models andwill be discussedfor a this course according to • matters learning algorithms in single neuron. rent plans: • Data preprocessing, Hebbian learning, and principal component analysis. University • Multilayer perceptron networks and their learning algorithms. of Technology, Espoo, Finland 1 • Model assessment and selection: generalization, validation, and regularization. • Radial-basis function networks. • Support vector machines. • Independent component analysis. • Self-organizing maps and learning vector quantization. • Processing of temporal information using feedforward and recurrent networks. Helsinki University of Technology, Espoo, Finland 12
  • 20. T-61.5140 Machine Learning: Advanced Probablistic Methods Jaakko Hollm´ n e Department of Information and Computer Science Helsinki University of Technology, Finland e-mail: Jaakko.Hollmen@tkk.fi Web: http://www.cis.hut.fi/Opinnot/T-61.5140/ January 17, 2008
  • 21. Course Material Lecture slides and lectures Lecture notes (aid the presentation on the lectures) Lecture notes (contain extra material) Course book Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer, 2006 Chapters 8,9,10,11, and 13 covered during the course Problem sessions Problems and solutions Demonstrations
  • 22. Passing the Course (5 ECTS credit points) Attend the lectures and the exercise sessions for best learning experience :-) Browse the material before attending the lectures and complete the exercises Complete the term project requiring solving of a machine learning problem by programming Pass the examination, next exam scheduled: Thursday, 15th of May, morning Requirements: passed exam and a acceptable term project, bonus for active participation and excellent term project (+1) Note: Jaakko Hollmén will give a presentation on the term project tomorrow
  • 23. Topics covered on the course Central topics Random variables Independence and conditional independence Bayes’s rule Naive Bayes classifier, finite mixture models, k-means clustering Expectation Maximization algorithm for inference and learning Computational algorithms for exact inference Computational algorithms for approximate inference Sampling techniques Bayesian modeling
  • 24. CLUSTER Dual Degrees • Macadamia has agreements for a dual degree currently with three other Master’s programmes in the CLUSTER network • The students will spend 1 year in both • Universitat Politècnica de Catalunya (UPC) • Universidade Técnica de Lisboa, Instituto Superior Técnico (IST)
  • 25. Feedback from the First Students Credit points: ?, 20, 25, 27 out of 30 “All goes well, the courses are all very interesting” “in general, everything is OK” “interest to work at the lab” “the layer between theory and running matlab toolboxes is missing” (Nikolaj’s course!) “some courses have more maths that I can handle at the moment, but this isn’t a bad thing” “some courses had overlapping schedules”
  • 26. More Information http://www.cis.hut.fi/macadamia/ Coordinator Tapani Raiko
  • 27. See you in Helsinki! • Mining and Learning with Graphs (MLG) workshop, July 4-5, 2008 • International Conference in Machine Learning (ICML), July 5-9, 2008 • Uncertainty in Artificial Intelligence (UAI), July 9-12, 2008