Calendar
This section provides the lecture topics for the course. This course also has a final exam, which is not included on the calendar.
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LEC # |
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TOPIC |
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1 |
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Introduction and overview |
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2 |
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Probability models and axioms |
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3 |
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Conditioning and Bayes' rule |
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4 |
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Independence (problem set 1 due) |
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5 |
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Counting |
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6 |
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Discrete random variables; probability mass functions; expectations (problem set 2 due) |
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7 |
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Conditional expectation; examples |
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8 |
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Multiple discrete random variables (problem set 3 due) |
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9 |
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Continuous random variables - I |
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10 |
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Continuous random variables - II (problem set 4 due) |
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Quiz 1, 50 minutes |
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11 |
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Continuous random variables and derived distributions |
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12 |
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More on continuous random variables, derived distributions, convolution |
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13 |
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Transforms (problem set 5 due) |
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15 |
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Iterated expectations, sum of of a random number of random variables |
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16 |
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Prediction; covariance and correlation (problem set 6 due) |
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17 |
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Bernoulli process |
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18 |
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Poisson process (problem set 7 due) |
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Quiz 2, 50 minutes |
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19 |
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Poisson process examples |
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20 |
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Markov chains - I |
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21 |
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Markov chains - II (problem set 8 due) |
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22 |
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Markov chains - III |
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23 |
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Weak law of large numbers (problem set 9 due) |
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24 |
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Central limit theorem |
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25 |
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Strong law of large numbers (problem set 10 due) |
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26 |
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Decision theory |
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27 |
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TBA |
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