this transformation, since both gradient ascent algorithm and Newton’s method can be applied to resolve maximization problems. 2 One approach to minimize the above objective function is gradient descent successive iterations is less than (or equal to) some threshold ϵ, i.e. |L+(θ) − L(θ)| ≤ ϵ (7) Try to resolve the logistic regression problem using gradient de- scent method with the initialization θ = 0, and

let it be zero. Hence, we have µl = �m i=1 ω(i) l x(i) �m i=1 ω(i) l To calculate φ, we have to resolve the following optimization problem max m � i=1 k � j=1 ω(i) j log φj s.t. k � j=1 φj = 1

worse performing half is discarded. If we start with configurations, it takes Hyperband attempts to resolve the problem of choosing by choosing brackets where each bracket starts with a pair of such that

and EM September 27, 2023 74 / 122 Laplace Smoothing (Contd.) This is unreasonable!!! How can we resolve this problem? Laplace smoothing: p(y) = �m i=1 1(y (i) = y) + 1 m + k pj(x | y) = �m i=1 1(y