• IBL - Instance Based Learning
• DT - Decision Trees
• COLT - Computational Learning Theory
• EBL - Explanation Based Learning
• ILP - Inductive Logic Programming
• GA - Genetic Algorithms
• EC - Evolutionary Computing
• RL - Reinforcement Learning
• DM - Data Mining
• USL - Unsupervised Learning
• HMM - Hidden Markov Models
• OLAP - Online Analytical Processing
• MBA - Market Basket Analysis

Many courses follow the text:
T Mitchell, Machine Learning, McGraw Hill, 1997.

When this is the case the topics are summarised as Mitchell with any additional topics also noted.

• New - This course is new in 2000
• Current - This course is ongoing with materials up to date
• Obsolete - The course materials are out of date

• Recommended: T M Mitchell, Machine Learning. McGraw-Hill International Student Edition (1997). ISBN 0-07-115467-1 (£21.99)
• Required: Selected papers from the machine learning literature.
• Online lecture slides

• J. Shavlik and T. Dietterich (eds): Reading in Machine Learning, Morgan Kaufmann, 1990
• J. Carbonell (ed): Machine Learning - paradigms and methods, MIT Press, 1990
• J. Ross Quinlan: C4.5 programs for machine learning, Morgan Kaufmann, 1993
• S. Muggleton (ed): Inductive logic programming, Academic Press, 1992
• S.M. Weiss en C.A. Kulikowski: Computer systems that learn, Morgan Kaufmann, 1991

• Mitchell: Machine Learning.
• Russell, Norvig: Artificial Intelligence.
• Fayyad et al: Advances in Knowledge Discovery and Data Mining.

• ANN, IBL, ES, EC, COLT
• Rule induction
• Bayesian statistics

• [Back el alli 97] T. Back, D. Fogel and Z. Michaelewicz, Handbook of Evolutionary Computation, Oxford University Press 1997.
• [Banzhaf et alli 98] W. Banzhaf, P. Nordin, R. E. Keller, F. D. Francone, Genetic Programming: An introduction, dpunkt-Verlag/Morgan Kaufmann, 1998.
• [Briscoe 96] G. Briscoe and T. Caelli, A compendium of Machine Learning, Volume 1: symbolic machine learning, Ablex, 1996.
• [Fausett 94] Laurene Fausett, Fundamentals of Neural Networks, Prentice-Hall, 1994.
• [Goldberg 89] David Goldberg, Genetic algoritmhs in search, optimization and machine learning, Addison-Wesley, 1989.
• [Langley 96] Pat Langley, Elements of Machine Learning, Morgan Kaufman, 1996.
• [Leake 96] David Leake, Case-Based Reasoning: experiences, lessons and future directions, MIT Press, 1996.
• [Mitchell 96] Melanie Mitchell, An introduction to Genetic Algorithms, MIT Press, 1996.
• [Mitchell 97] Tom Mitchell, Machine Learning, McGraw-Hill, 1997.
• [Nilsson 98] Nils Nilsson. Machine Learning, draft. 1998.
• [Russel & Norvig95] Stuart Russel & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 1995.

• Symbolic Computing
• Connectionist Models
• EC

• Definition of Distributed Artificial Intelligence(DAI).
• Motivations for MAS, Strong versus weak notions of agency.
• Intentional agent systems.
• Agent communication.
• Speech act theory.
• Collaboration, planning, belief desire intention (BDI) architectures.
• Agent oriented design, agent oriented programming and languages (Agent0,Agentalk) Multi-agent systems prototyping environment
• Industrial and commercial applications.

• Process Monitoring & Adjustment (SPC) Foundation
• Process Monitoring & Adjustment (SPC) Intermediate
• Measurement Process Evaluation Foundation
• Measurement Process Evaluation Intermediate
• Design of Multifactor Experiments Foundation
• Design of Multifactor Experiments Intermediate
• Process Simulation using SIMUL8
• Complex Decision Making using Expert Choice
• Decision Making for PFI/PPP Projects

• Anthony, M. & Biggs, N.: Computational Learning Theory. Cambridge University Press, 1992.
• Elomaa, T.: Koneoppiminen. Luentomuistiinpanot 1995-96.
• Kearns, M. & Vazirani, U.: An Introduction to Computational Learning Theory. MIT Press, 1994.
• Langley, P.: Elements of Machine Learning. Morgan Kaufmann, 1996.
• Michie, D., Spiegelhalter, D. & Taylor, C. (toim.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
• Mitchell, T.: Machine Learning. McGraw-Hill, 1997.
• Russell, S. & Norvig, P.: Artificial Intelligence: A Modern Approach, Part VI: Learning. Prentice Hall, 1995.
• Weiss, S. & Kulikowski, C.: Computer Systems that Learn. Morgan Kaufmann, 1991.

• JOHDANTO
• INDUKTIIVINEN OPPIMINEN
• LUOKITTELIJOIDEN OPPIMINEN
• PAC-MALLI
• PAC JA LUOKITTELIJAT
• SOVELLUKSIA

• Basic Concepts of Data Mining
• Data Mining Procedures
• Theoretical Foundation of Data Mining
• Data Mining Techniques
• Data Mining Applications

• Online lecture slides

• Symbolische Methoden
• Konnektionismus

• Data Warehousing / Data Marts
• Text Mining
• OLAP, DCT, ANN, ILP, USL

• DM, USL, EBL
• Memory-based (lazy) learning

• Data Mining for Business Advantage
• Data Mining with Clementine - Part 1
• Data Mining with Clementine - Part 2
• Clementine External Module Interface (CEMI)
• Automated Data MIning with Clementine Scripts

• Michalski, R., Carbonell, J., Mitchell, T. (eds), Machine learning, Tioga, 1983
• Online material for HyperCourse

• DM, IBL, DT, GA, ANN
• Complexity and regularity
• Information theory
• Data Compression
• Cluster regularities in examplar data
• Relational regularities and Relational data mining
• Compositional regularities

• Unger, S. und Wysotzki, F.: Lernfähige Klassifizierungssysteme (CAL2/3/5)
• Tom Mitchell: Machine Learning (gutes und aktuelles Lehrbuch)
• N. Lavrac und S. Dzeroski: Inductive Logic Programming (weiterführend)
• Michie, D. and Spiegelhalter, D. H. and Taylor, C. C.: Machine Learning, Neural and
• Statistical Classification (Vergleich und kurze Darstellung der wichtigsten Verfahren)

• Pat Langley: Elements of Machine Learning, Morgan-Kaufmann Publishers, Inc., ISBN 1-55860-301-8
• Tom Mitchell: Machine Learning, McGraw-Hill, 1997
• Kearns & Vazirani: An Introduction to Computational Learning, MIT Press, 1994
• Bergadano & Gunetti: Inductive Logic Programming, MIT Press, 1996

• Induction of Logical Conjunctions
• Induction of Threshold Concepts
• Induction of Competitive Concepts
• Construction of Decision Lists
• Revision and Extension of Inference Networks
• Formation of Concept Hierarchies
• Other Issues in Concept Induction
• Formation of Transition Networks
• Acquisition of Search Control Knowledge
• Formation of Macro-operators

• Russell & Norvig, 1995
• Quinlan & Cameron-Jones, 1995 Induction of Logic Programs: FOIL and Related Systems, New Generation Computing 13, 287--312.
• Fürnkranz, 1997, Pruning Algorithms for Rule Learning, Machine Learning, 27(2):139-171, May 1997.
• Cohen, 1994, Grammatically biased learning: learning logic programs using an explicit antecedent description language, Artificial Intelligence 68, 303--366.
• Selected Chapters from Stephen Muggleton (ed.) Inductive Logic Programming, Academic Press, 1992.
• Stephen Muggleton Inverse Entailment and Progol, in New Generation Computing, Vol. 13, pp. 245-286, 1995.
• Selected Chapters from Luc De Raedt (ed.) Advances in Inductive Logic Programming, IOS Press, 1995.

• Induction / Abduction / Deduction
• Data Representation
• COLT

• Foundations of Statistical Natural Language Processing by Manning and Schuetze, MIT Press 1999.
• Varied research papers

• Text analysis techniques

• ICS 214 Databases
• ICS 273 Machine Learning
• ICS 274 Probabilistic Learning
• ICS 278 Data Mining
• ICS 238A/Soc 201A: Descriptive Multivariate Statistics

• Visualization Techniques
• Exploratory Data Analysis
• Uncertainty and Statistics
• Presentation of DataSets
• Models and Patterns
• Score Functions
• Optimization and Search
• Classification Algorithms
• Clustering Algorithms
• Data Management Principles

• Machine Learning, Tom Mitchell, McGraw Hill, 1997

• Mitchell
• SVM
• Biological learning

• Jiawei Han, Micheline Kamber (2001) Data Mining: Concepts and Techniques, Morgan-Kaufmann, San Francisco
• Pieter Adriaans, Dolf Zantinge (1996) Data Mining, Addison-Wesley, Reading (A good low-level introduction. This should be available in the bookstore.)
• Dorain Pyle (1999) Data Preparation For Data Mining, Morgan Kaufmann (Data preparation for both data warehouses and data mining)

• Machine Learning, by Tom M. Mitchell.

• Version spaces
• Bayesian learning
• Hybrid learning
• DT, ANN, GA

• Bayesian Learning
• Boosting and Bagging
• DT, ANN, COLT, GA, SVM, IBL, ILP

• Decision Support Systems
• Data Visualisation
• OLAP

• Varied research papers

• Data Mining Techniques for Marketing, Sales, and Customer Support, by Michael Berry and Gordon Linoff, John Wiley, 1997.
• Various papers or online resources (provided in class or online).
• Recommended: The Data Webhouse Toolkit, by Ralph Kimball and Richard Merz, John Wiley, 2000.

• Web data mining
• Data preprocessing
• Clustering
• Information retreival and filtering
• Web information agents
• DM, MBA, USL

• Machine Learning, Tom Mitchell, McGraw-Hill, 1997.

• Mitchell

• Tom Mitchell, Machine Learning, McGraw Hill, 1997
• Various research papers

• Memory based learning
• Compression based learning
• Language learning
• Probabilistic Techniques
• DT, HMM, ANN, COLT

• C. M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press 97
• Tom Mitchell, Machine Learning, McGraw Hill, 1997

• Text: Machine Learning by T. Mitchell, McGraw Hill, 1997. Selected Data Mining papers from the recent literature

• Mitchell
• Basic Optimization Techniques for Learning
• Data Warehousing Techniques for learning and data mining.
• Statistical Techniques
• Computational Frameworks

• Data Warehousing in the Real World, Anahory/Murray, Addison-Wesley.

• Generalization, summarization and characterization.
• Discovery and analysis of patterns, trends and deviations.
• Mining knowledge in advanced or specialized database systems.
• Data mining models (decision trees, genetic algorithms, neural nets, etc).
• Data mining process (data preparation, defining a study, data cleaning, prediction.
• Enabling data mining through data warehouse.
• Integration of data mining tools with database systems.
• Data mining in distributed heterogeneous database systems.
• Data mining applications.

• prepare and organize data and leverage data warehouses to develop a strategy for data mining
• explain and compare methods and techniques to search for patterns in large databases, correlations, etc
• understand issues relating to mining image databases and other research issues
• formulate strategies to mine data and evaluate potential issues relating to performance, processes, pitfalls and solutions.

• Tom Mitchell. 1997. Machine Learning, New York: McGraw Hill.
• Web links to several research papers are provided.

• Business context for DM
• Ethical issues in DM

• Course Notes
• Online Demos

• Online lecture notes
• Various online papers

• Clustering
• Classification
• Bayesian approaches
• Feature selection
• Association rules
• Visualization
• Deviation Detection Techniques
• Commercial Tools
• DT, ANN, IBL

• Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurasamy, "Advances in Knowledge Discovery and Data Mining", AAAI Press/ The MIT Press, 1996.
• J. Ross Quinlan, "C4.5: Programs for Machine Learning", Morgan Kaufmann Publishers, 1993.
• Michael Berry and Gordon Linoff, "Data Mining Techniques (For Marketing, Sales, and Customer Support), John Wiley & Sons, 1997.
• Sholom M. Weiss and Nitin Indurkhya, "Predictive Data Mining: A Practical Guide", Morgan Kaufmann Publishers, 1998.
• Alex Freitas and Simon Lavington, "Mining Very Large Databases with Parallel Processing", Kluwer Academic Publishers, 1998.
• A. K. Jain and R. C. Dubes, "Algorithms for Clustering Data", Prentice Hall, 1988.
• V. Cherkassky and F. Mulier, "Learning From Data", John Wiley & Sons, 1998.

• Classification
• Clustering
• Association Rules
• Estimation/Regression
• Visualization
• Commercial Tools

• Bayesian Inference
• VC-dimension
• Learning finite automata
• COLT, IBL, ANN, HMM

• Neural Networks OR Decision Trees?
• Supervised Learning OR Clustering?
• Must a Choice be Made Between Accuracy OR Explainability?
• Is Preprocessing Really Necessary OR May I Just Build a Model?
• Are These Good Results OR Should Another Iteration Be Performed?
• Keep Asking Questions OR Get the Answers in Two Days?!

• Advances in Knowledge Discovery and Data Mining, Editors Usama M. Fayyad, Gregory
• Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, AAAI Press, 1996.

• Data Warehousing
• Clustering
• Association Rules
• Multimedia, Temporal and Spation Data
• Mining Distributed Databases
• OLAP

• Mitchell, T.M., Machine Learning, McGraw-Hill, 1997.
• Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
• Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996.
• Liu, H. and Motoda, H., Feature Selection for Knowledge Discovery and Data Mining, Kluwer, 1998.
• Michalsky, R.S., Bratko, I., and Kubat, M., Machine Learning and Data Mining, Wiley, 1998.
• Pyle, D., Data Preparation for Data Mining, Morgan Kaufmann, San Francisco, CA, 1999

• Mitchell
• Feature Selection
• Data Warehousing
• Overview of Comercial Tools

• Online notes
• Various research papers

• Association Rule Mining
• Query Flocks
• Searching the Web
• Clustering
• Matching Sequences
• Mining Event Sequences

• Neural Networks for Pattern Recognition, Christopher M. Bishop, Oxford, 1995.
• Machine Learning, Tom Mitchell, McGraw-Hill, 1997
• Introduction to Machine Learning, Nils Nilsson, in preparation

• ANN, SVM, RL, COLT
• Density Estimation
• (Semi) Linear Models
• Trees and Graphs
• Model Averaging

• Tom Mitchell, Machine Learning McGraw Hill, 1997.
• Lecture slides online

• Mitchell

• Online tutorial

• Identify appropriate problems for data mining
• Design a database for data mining
• Analyze and transform the data
• Use algorithms to build an accurate predictive model
• Evaluate the quality of your models

• U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
• Communications of the ACM, Special Issue on Data Mining, November 1996.
• Current papers from journals and magazines, provided during the course.

• Database Perspectives on Data Mining
• Associations and Rule Generation
• Data Warehousing and On-Line Analytical Processing
• Statistical Perspectives on Data Mining
• Classification and Clustering
• Genetic Algorithms in Data Mining
• Time-Series Clustering
• Sequential Patterns
• Deviation Detection
• Incremental Mining
• Scalability issues of Data Mining Algorithms
• Mining semistructured Data
• Latent Semantic Indexing
• Visualization of Data Mining Results
• Case Studies

• Tom Mitchell, Machine Learning , McGraw Hill, 1997.
• Additional reading material on current machine learning research.

• Mitchell
• Boosting and Bagging
• Learning by Querying
• HMM

• Machine Learning by Tom Mitchell, McGraw-Hill
• Chapter 1 of the book "Advances in Kernel Methods: Support Vector Machines" by B. Schlkopf, C. Burges and A. Smola

• RL,ANN,DT,kNN
• Naive Bayes
• Experimental Methodology

• Online text book
• Online slides

• Data warehouses
• Data preprocessing
• Association Rules
• Clustering
• Web Mining

• Required: T Mitchell, Machine Learning, McGraw Hill, 1997.
• Recommended: S Russell and P Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 1995.

• Mitchell
• SVM, DM
• New results: Boosting, Exponentiated Gradient
• New applications: Adaptive Software, Computational Molecular Biology

• Online lecture notes
• "Data Mining Techniques" by M. Berry and G. Linoff

• MBA, DM, DT, ANN, USL

• Mitchell, T.M. Machine Learning, McGraw-Hill, 1997.
• Langley, P. Elements of Machine Learning, Morgan Kaufmann, San Francisco, 1996.
• Quinlan, J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
• Kodratoff, R.S. Michalski (eds): Machine Learning: an AI Approach, Vol. III, Morgan Kaufmann, 1990.
• R.S. Michalski, J.G. Carbonell, T.M. Mitchell (eds): Machine Learning: an AI Approach, Vol. II, Morgan Kaufmann, Los Altos, CA, 1986.
• R.S. Michalski, J.G. Carbonell, T.M. Mitchell (eds): Machine Learning: an AI Approach, Vol. I, Tioga, Palo Alto, CA, 1983.

• Online slides
• Some research papers

• Bayesian Learning
• DT, USL, IBR, MBA, ANN

• Mitchell
• Stochastic, Propositional and Relational Learning.

• Textbook: Machine Learning, T. Mitchell, McGraw-Hill, 1997.
• Supplement 1: Machine Learning: A Multistrategy Approach, Vol. IV, (Eds.) R. Michalski and G. Tecuci, Morgan Kaufmann, 1994.
• Supplement 2: Machine Learning: Paradigms and Methods, (Ed.) J. G. Carbonell, MIT Press, 1990.

• Mitchell

• DT, IBL, COLT, ANN, EBL, GA, RL
• Learning processes: Schemas for classifying and analyzing learning systems
• Research methodologies, Speedup learning
• Filtering

• An Introduction to Computational Learning Theory, Kearns and Vazirani
• Machine Learning, Tom Mitchell

• DT, HMM, ANN, IBL, USL
• Heavy emphasis on COLT
• Learning Monomials
• Learning Decisions Lists
• Learning Finite Automata with queries
• Weak Learning and Boosting

• Data Mining Process
• Data Mining Applications
• Data Warehousing
• OLAP