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	  <font color="#693C8C" size="5px">PROBABILITY (DARTMOUTH COLLEGE, 2014)</font>

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    <p class="medium_column">LECTURE SLIDES</br></br>
	WEEK 1 - Basic concepts:</br>
<a href="/m20/summaries/lecture1.pdf">discrete probability</a>,</br>
<a href="/m20/summaries/lecture2.pdf">introduction to probability distributions</a>,</br>
<a href="/m20/summaries/lecture3.pdf">infinity and introduction to continuous probabilities</a>,</br>
WEEK 2 - Combinatorics</br>
<a href="/m20/summaries/lecture4.pdf">cumulative distibutions, permutations</a>,</br>
<a href="/m20/summaries/lecture5.pdf">counting combinations</a>,</br>
<a href="/m20/summaries/lecture6.pdf">binomial distribution and inclusion-exclusion</a>,</br>
WEEK 3 - Conditional Probability</br>
<a href="/m20/summaries/lecture7.pdf">discrete conditional probability</a>,</br>
<a href="/m20/summaries/lecture8.pdf">continuous conditional probability</a> + <a href="/m20/summaries/solutions.pdf">worksheet with solutions</a></br>
<a href="/m20/summaries/lecture9.pdf">Poisson distribution (and others)</a>,</br>
WEEK 4 -
</br><a href="/m20/summaries/lecture10solutions.pdf">functions of random variables</a>,</br>
<a href="/m20/summaries/lecture11.pdf">normal density</a>,</br>
<a href="/m20/summaries/summary.pdf">review for the midterm</a> + <a href="/m20/summaries/mockmidterm.pdf">mock midterm</a> with <a href="/m20/summaries/mockmidtermsolutions.pdf">solutions</a>.</br>
WEEK 5 - Expected Value, Standard Deviation and Variance</br>
<a href="/m20/summaries/lecture12.pdf">expected value</a>.</br>
<a href="/m20/summaries/lecture13.pdf">variance, coupon collector's problem</a>,</br>
<a href="/m20/summaries/lecture14.pdf">sums of independent random variables</a>,</br>

WEEK 6 - Law of Large Numbers and Central Limit Theorem</br>
<a href="/m20/summaries/lecture15worksheet.pdf">Law of Large Numbers</a> + <a href="/m20/summaries/lecture15.pdf">solutions</a>.</br>
<a href="/m20/summaries/lecture17.pdf">Central Limit Theorem: first glance</a>,</br>
<a href="/m20/summaries/lecture18.pdf">Central Limit Theorem: applications</a>,</br>
WEEK 7 - Joint Distributions</br>
<a href="/m20/summaries/lecture16.pdf">puzzles</a>,</br>
<a href=""></a>,</br>
<a href="/m20/summaries/lecture19.pdf">joint distributions</a>.</br>
WEEK 8 - Markov Chains</br>
<a href="/m20/summaries/lecture20.pdf">random walks</a>,</br>
<a href="/m20/summaries/lecture21.pdf">Markov chains, stationary distribution</a>,</br>
<a href="/m20/summaries/lecture22.pdf">absorbin Markov chains, matrix methods</a>.</br>
WEEK 9 - Markov Chains - continued</br>
<a href="/m20/summaries/lecture23.pdf">hitting times, irreducible and reversible Markov chains</a>,</br>
<a href="/m20/summaries/lecture24.pdf">Markov chains practice</a>,</br>
<a href="/m20/summaries/lecture25.pdf">Markov Chain Monte Carlo</a>.</br>
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<p class="text_column">ASSIGNMENTS </br></br>
<a href="/m20/assignments/hw1.pdf">Python primer + exercises</a></br>
<a href="/m20/assignments/hw2.pdf">week 1</a></br>
<a href="/m20/assignments/hw3.pdf">week 2</a></br>
<a href="/m20/assignments/hw4.pdf">week 3</a></br>
<a href="/m20/assignments/hw5.pdf">week 4</a></br>
<a href="/m20/assignments/hw6.pdf">week 5</a></br>
<a href="/m20/assignments/hw7.pdf">week 6</a></br>
<a href="/m20/assignments/hw8.pdf">week 7</a></br>
<a href="/m20/assignments/hw9.pdf">week 8 + 9</a></br>
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