I had a castle
And I wrote a cookbook
I juggle PhD life with three kids. It’s a balance of chaos, fun, and perspective.
I sail, bake sourdough bread, climb, ski, play games, and play music.
Lectures + Labs: Usually Thursdays, full-day session (except week 5 and 8 where it’s Tuesday)
📆 Rest of the week: Work on the lab report, study the material, strive to understand!
AI-log: One entry per week
Final exam: Sit-in exam, graded F–A
📚 Read the mandatory literature – It prepares you for the topics covered in lectures and labs.
🎓 Attend lectures and labs – They guide what’s examined. – If you skip them, make sure you master all assignments.
💻 Work through the labs carefully – They’re designed to teach you everything needed for the final exam.
🤖 Discuss with LLMs – Use them to deepen understanding and adapt concepts to your own learning style.
💬 Come to teaching hours – Ask questions, clarify doubts, and connect ideas.
Got questions, stuck in code, or want to chat about regression?
🕙 Mondays, 10:00–11:00 I’ll be available @Zoom
https://liu-se.zoom.us/my/lindmarkerhttps://liuonline.sharepoint.com/sites/Lisam_771A17_2025HT_7X/SitePages/TrainingHome.aspx
As I said — we won’t do a lot of math.
But we will use notation. And it helps to get comfy with it.
👉 Don’t be scared off by Greek letters or funny symbols: \[ \text{logit}(p_i) = \beta_0 + \sum_{j=1}^k \beta_j x_{ij} \]
\(x\) or \(y\) – a variable or value
\(\bar{x}\) – the mean (average) of \(x\)
\(n\) – number of observations
\(\sum\) – summation: e.g \(\sum_{i=1}^n x_i\) means “add all the \(x_i\) values from \(i = 1\) to \(n\)”
\(x_i\) – value of variable x for observation (row) i
\(x_{ij}\) – value in row i, column j of a data matrix
\(\widehat{y}\) – “y-hat” = estimated or predicted value
\(\alpha, \beta, \gamma, \delta, \varepsilon\)… – Greek letters we assign meaning to
\(\mathbb{E}[X]\) – expected value of \(X\)
\(f(x)\) – a function of x (a rule that maps inputs to outputs)
\(y = a + b x\) – a straight line