Mehdi Ghayoumi MSB rm 132 Ofc hr: Thur, a Machine Learning

Class Lectures: Section 1, Mon., 11a-12:15p, HDN 109 Section 2, Wed., 11a-12:15p, HDN 109 Class & Sections 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: , 2006 (corrected edition, 2009). Kevin Murphy, "Machine Learning - a Probabilistic Perspective", MIT Press, (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

Science is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about nature and the universe. knowledgeuniverse

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( ) = “apple” f( ) = “tomato” f( ) = “cow” Machine Learning

y = f(x) Machine Learning

Prediction Training Labels Training Images Training Image Features Learned model Machine Learning

Unsupervised “Weakly” supervised Fully supervised 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 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)... Machine Learning

Thank you!