CpSc 881: Machine Learning Introduction. 2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various sources.

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Presentation transcript:

CpSc 881: Machine Learning Introduction

2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various sources. The Copyright belong to the original authors. Thanks!

3 General Information Class Time: 5:45 PM ~ 8:30PM Monday Location: 119 McAdams Instructor:Dr. Feng Luo Office:210 McAdams Hall Phone: Office Hours:4:30PM ~ 5:30PM Monday Web site: arning.html

4 Prerequisite Familiarity with basic computer science principles and skills. Familiarity with the basic mathematics, like probability theory, basic linear algebra.

5 Text Book Tom Mitchell. Machine Learning, ISBN , WCB/McGraw-Hill Reference books: Ethem Alpaydin. Introduction to Machine Learning, ISBN: , the MIT Press. Nils J. Nilsson Introduction to Machine Learning, ( ook.html)

6 Grading Grading: Mid-term exam25 % Final exam 25 % Term project50 % Curved to A, B, C,D

7 Resources: Datasets UCI Repository: UCI KDD Archive: Statlib: Delve:

8 Tools Weka ( R ( Octave: A free matlab clone ( Machine Learning Tools in Java (

9 Resources: Journals Journal of Machine Learning Research Machine Learning Neural Computation Neural Networks IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association

10 Resources: Conferences International Conference on Machine Learning (ICML) ICML05: European Conference on Machine Learning (ECML) ECML05: Neural Information Processing Systems (NIPS) NIPS05: Uncertainty in Artificial Intelligence (UAI) UAI05: Computational Learning Theory (COLT) COLT05: International Joint Conference on Artificial Intelligence (IJCAI) IJCAI05: International Conference on Neural Networks (Europe) ICANN05:

11 What is Machine Learning Definition – Computing Dictionary: The ability of a machine to improve its performance based on previous results.

12 Why Machine Learning Human expertise does not exist (navigating on Mars) Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (user biometrics)

13 Three niches for machine learning Data Mining: using historical data to improve decisions Medical records -> medical knowledge Software application we can NOT program by hand Autonomous driving Speech recognition Self customizing programs News reader that learns user interests

14 Applications of Machine Learning Information filtering/classification

15 Applications of Machine Learning Playing games

16 Applications of Machine Learning Robotics

17 Applications of Machine Learning fault detection/monitoring technical systems

18 Applications of Machine Learning bioinformatics

19 Applications of Machine Learning image classification picture processing

20 Applications of Machine Learning Text/language processing, classification, visualization,...

21 Applications of Machine Learning

22 Connections of Machine Learning ML AI Neurobiology Control Statistics Optimization Information theory Psychology Philosophy