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Machine Learning Mehdi Ghayoumi MSB rm 132 [email protected] Ofc hr: Thur, 11-12 a Machine Learning Class & Sections Class Lectures: Section 1, Mon., 11a-12:15p, HDN 109 Section 2, Wed., 11a-12:15p, HDN 109 Machine Learning What I expect from you: • Feedback and collaborate in class • Regular attendance • Hard work • Memorization of key concepts and Creativity • Self - learning Machine Learning My Goals: • Give you some theoretical knowledge • Publish our final approach as papers • Promote our Smartness. Machine Learning Your Goals: • Your First Bonus. Machine Learning • Final:(1) 30%, • Theory Assignments: (2)10%, • Programming Assignments: (2)10%, • Programming project: (1) 30%, • Participation: ( All classes)10%, • Quiz: (2) 10%, A > 92%, A- > 85%, B+ > 80%, B > 75%, B- > 70%, C+> 65%, C > 60%, C- > 55%, D+ > 53%, D > 50% Machine Learning • Final: 30%, Last week( Last session- Last week): • Class slides and their examples, • Class and home assignments, • Class discussions. Machine Learning • Theory assignments: 2-10%: Some Weeks homework assign, (Wednesdays), No late homework accepted, Written solutions must be your own, Machine Learning • Programming Assignments: 2- 10%: Machine Learning • Programming project: 30%, • First Report 5% • Second Report 5% • Project 20% 1.Team project only. 2. A list of topics will be provided. 3.The project work is collaborative. Machine Learning • Class Participation: 10% Machine Learning • Quiz: 2- 10% Machine Learning References: "Pattern Recognition and Machine Learning", Christopher M. Bishop, Publisher: Springer Verlag, ISBN: 978-0387-31073-2, 2006 (corrected edition, 2009). Kevin Murphy, "Machine Learning - a Probabilistic Perspective", MIT Press, 2012. (online via Kent Library) Machine Learning Send me these information: 1. Level of your programming proficiency, 2. Languages and databases that you know. 3. Name of group members Machine Learning Machine Learning Science is a systematic enterprise that builds and organizes knowledge explanations the universe. and in the predictions form about of testable nature and Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning Why “Learn” ? • Machine learning is programming computers to optimize a performance criterion using example data or past experience. • There is no need to “learn” to calculate payroll • Learning is used when: – 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) Machine Learning What is Machine Learning? • Optimize a performance criterion using example data or past experience. • Role of Statistics: Inference from a sample • Role of Computer science: Efficient algorithms to – Solve the optimization problem – Representing and evaluating the model for inference Machine Learning • Apply a prediction function to a feature representation of the image to get the desired output: f( f( f( ) = “apple” ) = “tomato” ) = “cow” Machine Learning y = f(x) Machine Learning Training Labels Training Images Image Features Image Features Training Learned model Learned model Prediction Machine Learning Machine Learning Unsupervised “Weakly” supervised Fully supervised Machine Learning Machine Learning Machine Learning • • • • • • • • SVM Neural networks Naïve Bayes Logistic regression Decision Trees K-nearest neighbor RBMs Etc. Machine Learning Resources: Journals • • • • • • • • • Journal of Machine Learning Research www.jmlr.org 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 ... Machine Learning Resources: Conferences • International Conference on Machine Learning (ICML) • European Conference on Machine Learning (ECML) • Neural Information Processing Systems (NIPS) • Uncertainty in Artificial Intelligence (UAI) • Computational Learning Theory (COLT) • International Joint Conference on Artificial Intelligence (IJCAI) • • International Conference on Neural Networks (Europe) ... Thank you!