Negative Binomial Distribution with finite number of trials Suppose a user tosses a coin $n$ times. How do I compute the expected value of the number of heads before the user sees $k$ tails, ($k < n$)?
This looks somewhat like Negative Binomial Distribution (NBD). However, unlike this problem with a finite number of tosses ($n$), there no cap on the number of tosses in a classic NBD. I am wondering how that derivation would look?
 A: "before the user sees $k$ tails, ($k < n$)"
thus last tail can range from position $k\;$ to position $(n-1)$,
which means number of heads can range from $0$ to $(n-k-1)$
Thus by symmetry, the expected number of heads before seeing $k$ tails $= \frac{n-k-1}2$
A: I left a number of comments, because I was confused about the wording of the problem.  After reading this Negative Binomial Distribution article, I deleted my comments.  The smoke had cleared.

Suppose a user tosses a coin $n$ times. How do I compute the expected value of the number of heads before the user sees $k$ tails, $(k<n)$?

As is, the wording of this problem is difficult to interpret.
One interpretation is that you are supposed to let $k$ range from $(1)$ to $(n)$.  For each value of $k$, you are to compute the probability of there being $(k-1)$ successes (i.e. Tails) in the first $(n-1)$ trials (i.e. Coin Flips), and a success on the $n$th trial.
The probability of this occurring is
$$\frac{\binom{n-1}{k-1}}{2^n}. \tag1 $$
In such a situation, there will be $(n-k)$ failures (i.e. Heads).  Therefore, the expected number of failures is
$$\large{\sum_{k=1}^n} ~~\frac{(n-k) \times \binom{n-1}{k-1}}{2^n} $$
$$ = \dfrac{1}{2^n} ~\times \sum_{k=1}^n ~~\left[(n-k) \times \binom{n-1}{k-1}\right] $$
$$ = \dfrac{1}{2^n} ~\times \sum_{k=1}^{n-1} ~~\left[(n-k) \times \binom{n-1}{k-1}\right] $$
$$~~\left[ ~\text{since, when} ~(k = n), ~\text{the} ~(n - k)
~\text{term is} ~= 0\right]$$
$$ = \dfrac{1}{2^n} ~\times \large{\sum_{k=1}^{n-1}} 
~~\left\{ ~(n-k) \times \frac{(n-1)!}{[(k-1)!] ~[(n-k)!]}
 ~\right\}$$
$$ = \dfrac{1}{2^n} ~\times \large{\sum_{k=1}^{n-1}} 
~~\left\{ ~\frac{(n-1)!}{[(k-1)!] ~[(n-k-1)!]}
 ~\right\}$$
$$ = \dfrac{n-1}{2^n} ~\times \large{\sum_{k=1}^{n-1}} 
~~\left\{ ~\frac{(n-2)!}{[(k-1)!] ~[(n-k-1)!]}
 ~\right\}$$
$$ = \dfrac{n-1}{2^n} ~\times \sum_{k=1}^{n-1} 
~\binom{n-2}{k-1} $$
$$ = ~~\left\{\text{by re-indexing} ~k ~\right\} ~~\dfrac{n-1}{2^n} ~\times \sum_{k=0}^{n-2} 
~\binom{n-2}{k} $$
$$ = ~~\dfrac{n-1}{2^n} ~\times 2^{(n-2)} $$
$$~~\left[ ~\text{since} ~~(1 + 1)^{(n-2)} = 
\sum_{k=0}^{n-2} \binom{n-2}{k} ~\right] $$
$$ = \frac{n-1}{4}. \tag2 $$

Another possible interpretation is that you are supposed to assume that $n$ is some fixed positive integer, and that $k$ is some fixed element in $\{1,2,\cdots,n\}$, and you are supposed to compute the expected number of failures, in this isolated case.
Here, if $k$ is some fixed element, then from the previous analysis in this answer, the expected number of failures is
$$(n - k) ~\times ~\frac{\binom{n-1}{k-1}}{2^n}. \tag3 $$
(3) above is based on the assumption that the probability of success (i.e. Tails) $~= \frac{1}{2} = ~$ the probability of failure (i.e. Heads).
Personally, I see no way of simplifying the expression in (3) above.
Further, the expression in (3) above may be generalized by assuming that the probabilities of success and failure are $(p)$ and $(q)$, respectively, with $(p + q = 1).$
Then, the expression in (3) above changes to
$$(n - k) ~\times ~\binom{n-1}{k-1} ~p^k ~q^{(n-k)}. \tag4 $$

Finally, there is a 3rd interpretation, which is detailed below.  These are the only $(3)$ interpretations of the problem that I can conjure.  After reading the "Negative Binomial Distribution" article that I linked to at the start of my answer, I am unsure which of the three interpretations was intended by the problem composer.
Anyway...
The 3rd interpretation is that you are supposed to assume that $k$ is some fixed positive integer, and that $n$ goes from $k$ to infinity.  That is, you are supposed to compute the expected number of Heads, until $(k)$ Tails, regardless of how long it takes.
Using the previous analysis, under this interpretation of the problem, you are supposed to assume that $k$ is a fixed positive integer, and you are supposed to compute
$$\large{\sum_{n=k}^\infty} ~\left[ ~(n - k) ~\times ~\frac{\binom{n-1}{k-1}}{2^n} ~\right]. \tag5 $$
An informal approach that I am unsure is either accurate (or valid) is to reason that in $(2k-2)$ coin flips, you expect to get $(k-1)$ Tails and (therefore) $(k-1)$ Heads.  This event would have to be scaled by $(1/2)$, since you must also get a Tails on the very next coin flip.
So, you could (informally, blindly) guess that the expected number of Heads is $\dfrac{k-1}{2}.$
$\color{\red}{\text{In fact, the above guess-work is wrong.}}$
A formal approach is shown below.

In (5), with $k$ fixed and $n$ going to infinity, the ratio of successive terms is
$$\frac{n}{2(n-k)} \to \frac{1}{2},$$
so the expression in (5) above is convergent, as expected.
After failing to find some other elegant way of attacking the expression in (5), I decided to use recursion (AKA the uneducated person's substitute for Markov Chains).
Let $E(k)$ denote the expected number of trials (i.e. coin flips) required for $k$ successes (i.e. Tails).
This will allow the expected number of failures to be computed as $E(k) - k.$
Then
$\displaystyle 
E(1) = \left[ ~\frac{1}{2} \times 1 ~\right] ~+
 ~\left[ ~\frac{1}{2} \times ~\left\langle ~1 + E(1) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
E(1) = 2.$
$\displaystyle 
E(2) = \left[ ~\frac{1}{2} \times ~\left\langle ~1 + E(1) ~\right\rangle ~\right] ~+
 ~\left[ ~\frac{1}{2} \times ~\left\langle ~1 + E(2) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
E(2) = 4.$
Forming the inductive hypothesis that $E(k) = 2k$, you have that
$\displaystyle 
E(k+1) = \left[ ~\frac{1}{2} \times ~\left\langle ~1 + E(k) ~\right\rangle ~\right] ~+
 ~\left[ ~\frac{1}{2} \times ~\left\langle ~1 + E(k+1) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
\frac{E(k+1)}{2} = \frac{2k + 1}{2} + \frac{1}{2} = \frac{2k+2}{2} \implies $
$\displaystyle 
E(k+1) = 2k + 2,~$ which proves the hypothesis.
Therefore, $~E(k) = 2k \implies~$ the expected number of Heads required until the $k$th tail is $E(k) - k = k.$

It only remains to repeat the above analysis, under the assumption that the probability of success and failure is $~(p)~$ and $~(q = 1-p),~$ respectively.
$\displaystyle 
E(1) = \left[ ~p \times 1 ~\right] ~+
 ~\left[ ~q \times ~\left\langle ~1 + E(1) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
p \times E(1) = 1 \implies E(1) = \frac{1}{p}.$
$\displaystyle 
E(2) = \left[ ~p \times ~\left\langle ~1 + E(1) ~\right\rangle ~\right] ~+
 ~\left[ ~q \times ~\left\langle ~1 + E(2) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
p \times E(2) = p + pE(1) + q = 2 \implies E(2) = \frac{2}{p}.$
Forming the inductive hypothesis that $E(k) = \dfrac{k}{p}$, you have that
$\displaystyle 
E(k+1) = \left[ ~p \times ~\left\langle ~1 + E(k) ~\right\rangle ~\right] ~+
 ~\left[ ~q \times ~\left\langle ~1 + E(k+1) ~\right\rangle ~\right] ~\implies $ 
$\displaystyle 
p \times E(k+1) = 1 + \left[ ~p \times \frac{k}{p} ~\right]
= (k + 1) \implies E(k+1) = \frac{k+1}{p},$ 
which proves the hypothesis.
Therefore, $~\displaystyle E(k) = {k}{p} \implies~$ the expected number of failures required until the $k$th  success is 
$\displaystyle E(k) - k = \frac{k}{p} - k = \frac{k - kp}{p} = \frac{kq}{p}.$
