## Courses I took in Mcgill

COMP 652 Machine Learning
Linear models: linear and polynomial regression, overfitting, model selection, logistic regression, Naive Bayes
Non-linear models: decision trees, instance-based learning, boosting, neural networks
Support vector machines and kernels
Computational learning theory
Structured models: graphical models, deep belief networks
Unsupervised learning: K-means, expectation maximization, PCA and other dimensionality reduction methods
Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models
Reinforcement learning
COMP 690 Probabilistic Analysis of Algorithms
Binary search trees, Divide-and-conquer, Randomized algorithms, Conditional branching processes, Random graphs, Combinatorial search problems, Markov chains, Random walks
MATH 556 Mathematics Statistics I
Univariate and Multivariate Distributions
Transformations and Expectations
Families of distributions: Location-Scale Families, Exponential Families, Convolution Families and Exponential Dispersion Models, Hierarchical Models
Some Inequalities: Markov’s inequality, Chebyshev’s inequality, Chernoff bounds, Cauchy-Schwarz Inequality, Jensen’s Inequality
Sampling Distributions: Sampling from a Location-Scale Family, Exponential Family, Normal Family
Convergence concepts: The Weak Law of Large Numbers, The Strong Law of Large Numbers, Weak Convergence, A Central Limit Theorem, The Delta Method
Random Number Generation
MATH 557 Mathematics Statistics II
Data Reduction and Summary: Sufficiency and Ancillarity, The Likelihood Principle
Point Estimation: Methods of Finding Estimators, Methods of Evaluating Estimators
Hypothesis Testing: Objectives, Test Construction, Test Evaluation, Interval Estimation, Methods of Finding Interval Estimators, Methods of Evaluating Interval Estimators
Asymptotic Considerations: Point Estimation, Robustness, Hypothesis Testing, Interval Estimation
Inference Paradigms: Comparing Frequentist and Bayesian Approaches
COMP 521 Modern Computer Games
Game genres, theories of fun, Storytelling and narratives, Narrative modelling and analysis, Game engines, Game physics: physical simulation, approximation techniques, Collision detection and response, Path-finding, Group behaviour, NPCs, Opponents and strategy, Scripting and testing. Multiplayer games, Networking and distributed game design, Consistency, dead-reckoning, timing, Massively multiplayer games: persistence, scalability, Cheating, Concurrency
FRSL 302: French Listening Comp & Oral Exp 1
FRSL 303: French Listening Comp & Oral Exp 2