INF 552 Machine Learning for Data Informatics: Spring Mar 06, 2017 (21 of 21)

Опубликовано: 04 Ноябрь 2024
на канале: Carl Mullins USC

USC Viterbi School of Engineering
USC Institute for Creative Technologies

INF 552 Machine Learning for Data Informatics: Spring Mar 06, 2017 (21 of 21)

Instructors: Professor Stefan Scherer

INF 552 Machine Learning for Data Informatics

Spring 2017—Monday & Wednesday—Time: 10-11:50 AM

Syllabus: INF 552 Machine Learning for Data Informatics. Spring 2017
https://web-app.usc.edu/soc/syllabus/...

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Week 1: Introduction to Machine Learning

• Introduction to Machine Learning
• Machine Learning examples
• Introduction to Machine Learning with Python and MATLAB (1/2)

Week 2: Supervised Learning

• Introduction to supervised learning
• PAC learnable
• VC dimension
• Machine Learning Experiments
• Introduction to Machine Learning with Python and MATLAB (2/2)

Week 3: Nonparametric Methods

• Bayesian Decision Theory
• Nonparametric methods
• Data “Hands-on”

Week 4: Decision Trees
• Introduction to decision trees
• Entropy measures

Week 5: Linear Discrimination and Neural Networks
• Linear Discrimination
• Perceptron
• Multilayer Perceptron
• Recurrent Neural Networks

Week 6: User study setup and Evaluation
• Study setup
• Statistical Testing

Week 7: Unsupervised Learning
• Unsupervised Learning
• Clustering
• Competitive Learning
• Semi-supervised Learning

Week 8: Parametric and Multivariate Methods
• Parametric and Multivariate Methods
• Regression
• Evaluating Regression

Week 9: Midterm
• Midterm

Week 10: Kernel Machines
• Support Vector Machines
• GMM UBM SVM

Week 11: Kernel Machines
• Bayesian Networks
• Belief Propagation

Week 12: Kernel Machines
• Hidden Markov Models Principles
• Viterbi Algorithm
• Applications of HMM

Week 13: Deep Learning and Combining Learners
• Auto-encoders
• Restricted Boltzmann Machines
• Ensemble Classifiers/Classifier fusion

Week 14: Deep Learning cont’d
• Long Short-term Memory Networks
• Convolutional Neural Networks

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