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Week - 1 |
Safety, Reliability, Quality and Test Data in Rail Systems, Introduction to Data Analysis |
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Week - 2 |
Fundamentals of Probability, Probability Distributions, Univariate and Multivariate Distributions |
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Week - 3 |
Estimation Theory, Biased and Unbiased Estimations, Cramer Rao Lower Bound |
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Week - 4 |
Bayesian Decision Systems, Maximum Likelihood Method, Least Squares Method |
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Week - 5 |
Linear Regression, Linear Models |
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Week - 6 |
Designing Learning Systems, Training, Testing, Supervised Learning |
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Week - 7 |
Decision Trees, Basic Decision Tree Learning Algorithms, Overfitting Problem |
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Week - 8 |
Artificial Neural Networks, Mathematical Background, Training and Testing |
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Week - 9 |
Artificial Neural Networks, Applications, Avoding Overfitting the Data |
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Week - 10 |
Unsupervised Learning, Clustering, K-means Clustering |
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Week - 11 |
Principal Component Analysis and Applications |
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Week - 12 |
Hidden Markov Models |
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Week - 13 |
Importance of Data Analysis and Machine Learning Applications in Rail Systems |
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Week - 14 |
Machine Learning Applications in Rail Systems |